We’re defining the function as in the ‘Greenhouse gas emissions model’ document. Please look there for more information about the original model design.

Here we load the library and the data. The data contains ranges of possible values for the simulation. Each variable is represented with a confidence interval range and a specification of the distribution shape.

Running the model with scenarios

The normal mcSimulation function takes random draws for each of the input variables from the distributions specified in the input table. The distributions that are used are the same for every run of the Monte Carlo simulation. Sometimes it’s desirable to specify particular scenarios a priori, e.g. to simulate outcomes for particular farm types, climate scenarios etc. To do this, we would normally have to define separate input tables and run separate simulations. This is quite inconvenient. Here, we usep a function that can do this job automatically.

The scenario_mc function in the decisionSupport package (Luedeling et al. (2021)) consists of a wrapper around the mcSimulation function. The function is able to modify the input table according to a scenario file we provide. This file contains information on which variables should be modified for each scenario and how many model runs should be included in the simulation. The function is essentially the same as the mcSimulation function, but with an additional scenarios option which imports the contents of a .csv file.

The .csv file for the scenarios option specifies variables that should be modified for each of the scenarios, as well as the number of model runs for for it. Each column beyond the first two columns in the file contain information for a particular scenario. This is restricted to the parameters that should be changed for this scenario. Here’s an example scenarios file:

knitr::kable(read.table("data/scenarios.csv", sep = ",", fileEncoding = "UTF-8-BOM",header=TRUE))
Variable param Scen1 Scen2
Runs x 100 300
N_animal_type_1 both 100 4000
N_animal_type_2 lower 100 10
N_animal_type_2 upper 1000 20
N_animal_type_2 distribution posnorm norm

The first row (“Runs” in the first column) indicates the number of runs. Each subsequent row indicates which value from the input table should be modified, so the string in the first column has to correspond to a variable name in the input table. The second column indicates which values should be changed, with options being “lower,” “upper,” “distribution” and “both.” If “both” is selected, both the lower and upper bounds are modified (this should only be done for constants).

Kenyaherds<-read.table("data/Kenya_baseline_individual_cow_wNotes.csv", sep = ",",
                       fileEncoding = "UTF-8-BOM", header=TRUE)

# count the number of animals for each type

animals<-table(Kenyaherds[,c("QQNO","animal_type")])

variables_to_update_const<-c(paste0("N_animal_type_",1:12),
                             "feeding_system")

scenarios<-data.frame(Variable=variables_to_update_const,param="both")

variables_to_update_posnorm<-c(paste0("milk_yield_",4:6),
                             paste0("Cp_rate_",4:6),
                             "feed_prod_CO2",
                             "feed_trans_CO2",
                             paste0("live_weight_LW_",1:12),
                             paste0("weight_gain_WG_",1:12),
                             "DE_perc",
                             "perc_crude_protein_diet")

scenarios<-rbind(scenarios,data.frame(Variable=rep(variables_to_update_posnorm,each=3),param=c("lower","upper","distribution")))


for(i in 1:nrow(animals))
  {coln<-paste0("Farm_",row.names(animals)[i])
   scenarios[,coln]<-NA
     scenarios[which(scenarios$Variable %in% paste0("N_animal_type_",1:12)),coln]<-animals[i,]
}

# for some animal-scale variables (e.g. milk yield), we need averages per farm

### error estimates for variables
milk_yield_error<-0.275 # Migose et al. (2020, https://www.sciencedirect.com/science/article/abs/pii/S1871141319307371 )
feed_trans_error<-0.25 # Vellinga et al (2013, https://edepot.wur.nl/254098)
feed_prod_error<-0.25 # Vellinga et al (2013, https://edepot.wur.nl/254098)
live_weight_error<-0.145 # Goopy et al. (2018, https://www.publish.csiro.au/an/an16577 ) 
weight_gain_error<-0.145 # Goopy et al. (2018, https://www.publish.csiro.au/an/an16577 ) 
digestible_energy_error<-0.1
crude_protein_error<-0.1

# farm-scale variables

Farms<-unique(Kenyaherds$QQNO)
  
for (f in Farms)
  {coln<-paste0("Farm_",f)
  # update feeding system 
  scenarios[which(scenarios$Variable == "feeding_system"),coln]<-
     median(Kenyaherds[which(Kenyaherds$QQNO==f),"system"])
  
  # update digestible energy (mean for all animals for now) (with error)
  scenarios[which(scenarios$Variable == "DE_perc"),coln][1:2]<-
     mean(Kenyaherds[which(Kenyaherds$QQNO==f),"dig_energy"])*
                           c(1-digestible_energy_error,1+digestible_energy_error)
  
  # update crude protein (mean for all animals for now) (with error)
  scenarios[which(scenarios$Variable == "perc_crude_protein_diet"),coln][1:2]<-
     mean(Kenyaherds[which(Kenyaherds$QQNO==f),"CP."])*
                           c(1-crude_protein_error,1+crude_protein_error)
  
  # update feed production CO2 (with error)
   scenarios[which(scenarios$Variable == "feed_prod_CO2"),coln][1:2]<-
     mean(Kenyaherds[which(Kenyaherds$QQNO==f),"feed_kgCO2"])*
                           c(1-feed_prod_error,1+feed_prod_error)
  # update feed transport CO2 (with error)
   scenarios[which(scenarios$Variable == "feed_trans_CO2"),coln][1:2]<-
     mean(Kenyaherds[which(Kenyaherds$QQNO==f),"feed.transport_kgCO2"])*
                           c(1-feed_trans_error,1+feed_trans_error)
  # update milk yield for each cow type (animal type 4-6) (with error)
   for(typ in 4:6)
     if(sum(Kenyaherds$QQNO==f&Kenyaherds$animal_type==typ)>0)
       {scenarios[which(scenarios$Variable==paste0("milk_yield_",typ)),coln][1:2]<-
         mean(Kenyaherds[which(Kenyaherds$QQNO==f&Kenyaherds$animal_type==typ),
                         "milk_yield"])*c(1-milk_yield_error,1+milk_yield_error)
       scenarios[which(scenarios$Variable==paste0("CP_rate_",typ)),coln][1:2]<-
         mean(Kenyaherds[which(Kenyaherds$QQNO==f&Kenyaherds$animal_type==typ),
                         "Cp"]/0.1) *c(1,1)
   
     }
  # update live weight and weight gain for each animal type (with error)
   for(typ in 1:12)
     if(sum(Kenyaherds$QQNO==f&Kenyaherds$animal_type==typ)>0)
       {scenarios[which(scenarios$Variable==paste0("live_weight_LW_",typ)),coln][1:2]<-
         mean(Kenyaherds[which(Kenyaherds$QQNO==f&Kenyaherds$animal_type==typ),
                         "live_weight_LW"])*c(1-live_weight_error,1+live_weight_error)
       scenarios[which(scenarios$Variable==paste0("weight_gain_WG_",typ)),coln][1:2]<-
         mean(Kenyaherds[which(Kenyaherds$QQNO==f&Kenyaherds$animal_type==typ),
                         "weight_gain_WG"])*c(1-weight_gain_error,1+weight_gain_error)
     }
   
   for(varia in variables_to_update_posnorm)
   if(sum(is.na(scenarios[which(scenarios$Variable==varia),coln][1:2]))==0)   
     if(!equals(scenarios[which(scenarios$Variable==varia),coln][1],
               scenarios[which(scenarios$Variable==varia),coln][2]))
        scenarios[which(scenarios$Variable==varia&
                   scenarios$param=="distribution"),coln]<-"posnorm" else
            scenarios[which(scenarios$Variable==varia&
                   scenarios$param=="distribution"),coln]<-"const"         
   
   }
  
write.csv(scenarios,"data/farm_scenarios.csv",row.names = FALSE)

Here’s the resulting file:

knitr::kable(read.table("data/farm_scenarios.csv", sep = ",", fileEncoding = "UTF-8-BOM",header=TRUE))
Variable param Farm_1 Farm_2 Farm_3 Farm_4 Farm_5 Farm_6 Farm_7 Farm_8 Farm_9 Farm_10 Farm_11 Farm_12 Farm_13 Farm_14 Farm_15 Farm_16 Farm_18 Farm_19 Farm_20 Farm_21 Farm_22 Farm_23 Farm_24 Farm_25 Farm_26 Farm_27 Farm_28 Farm_29 Farm_30 Farm_31 Farm_32 Farm_33 Farm_34 Farm_35 Farm_36 Farm_37 Farm_38 Farm_39 Farm_40 Farm_41 Farm_42 Farm_43 Farm_44 Farm_45 Farm_46 Farm_47 Farm_48 Farm_49 Farm_50 Farm_51 Farm_52 Farm_53 Farm_54 Farm_55 Farm_56 Farm_57 Farm_58 Farm_59 Farm_60 Farm_61 Farm_62 Farm_63 Farm_64 Farm_65 Farm_66 Farm_67 Farm_68 Farm_69 Farm_70 Farm_71 Farm_72 Farm_73 Farm_74 Farm_75 Farm_76 Farm_77 Farm_78 Farm_79 Farm_80 Farm_81 Farm_82 Farm_83 Farm_84 Farm_85 Farm_86 Farm_87 Farm_88 Farm_89 Farm_91 Farm_92 Farm_93 Farm_94 Farm_95 Farm_96 Farm_97 Farm_98 Farm_99 Farm_100 Farm_101 Farm_102 Farm_103 Farm_104 Farm_105 Farm_106 Farm_107 Farm_108 Farm_109 Farm_110 Farm_111 Farm_112 Farm_113 Farm_114 Farm_115 Farm_116 Farm_117 Farm_118 Farm_119 Farm_120 Farm_121 Farm_122 Farm_123 Farm_124 Farm_125 Farm_126 Farm_127 Farm_128 Farm_129 Farm_130 Farm_131 Farm_132 Farm_133 Farm_134 Farm_135 Farm_136 Farm_137 Farm_138 Farm_139 Farm_140 Farm_141 Farm_142 Farm_143 Farm_144 Farm_145 Farm_146 Farm_147 Farm_148 Farm_149 Farm_150 Farm_151 Farm_152 Farm_153 Farm_154 Farm_155 Farm_156 Farm_157 Farm_158 Farm_159 Farm_160 Farm_161 Farm_162 Farm_164 Farm_166 Farm_167 Farm_168 Farm_169 Farm_170 Farm_171 Farm_172 Farm_173 Farm_174 Farm_175 Farm_177 Farm_178 Farm_179 Farm_180 Farm_181 Farm_182 Farm_183 Farm_184 Farm_185 Farm_186 Farm_187 Farm_188 Farm_189 Farm_190 Farm_191 Farm_192 Farm_193 Farm_194 Farm_195 Farm_196 Farm_197 Farm_198 Farm_199 Farm_201 Farm_202 Farm_203 Farm_204 Farm_205 Farm_206 Farm_207 Farm_208 Farm_209 Farm_210 Farm_211 Farm_212 Farm_213 Farm_214 Farm_215 Farm_216 Farm_217 Farm_218 Farm_219 Farm_220 Farm_221 Farm_222 Farm_223 Farm_224 Farm_225 Farm_226 Farm_227 Farm_228 Farm_229 Farm_230 Farm_231 Farm_232 Farm_233 Farm_234 Farm_235 Farm_236 Farm_237 Farm_238 Farm_239 Farm_240 Farm_241 Farm_242 Farm_243 Farm_244 Farm_245 Farm_246 Farm_248 Farm_249 Farm_250 Farm_251 Farm_252 Farm_253 Farm_254 Farm_255 Farm_256 Farm_258 Farm_259 Farm_260 Farm_261 Farm_262 Farm_263 Farm_264 Farm_265 Farm_266 Farm_267 Farm_268 Farm_269 Farm_270 Farm_271 Farm_272 Farm_273 Farm_274 Farm_276 Farm_277 Farm_278 Farm_279 Farm_280 Farm_282 Farm_283 Farm_285 Farm_286 Farm_287 Farm_288 Farm_289 Farm_290 Farm_291 Farm_292 Farm_293 Farm_294 Farm_295 Farm_296 Farm_297 Farm_298 Farm_299 Farm_300 Farm_301 Farm_303 Farm_304 Farm_305 Farm_306 Farm_307 Farm_308 Farm_309 Farm_310 Farm_311 Farm_312 Farm_313 Farm_314 Farm_315 Farm_316 Farm_317 Farm_318 Farm_319 Farm_321 Farm_322 Farm_323 Farm_324 Farm_325 Farm_326 Farm_327 Farm_328 Farm_329 Farm_330 Farm_331 Farm_332 Farm_333 Farm_334 Farm_335 Farm_336 Farm_337 Farm_338 Farm_339 Farm_340 Farm_341 Farm_342 Farm_343 Farm_344 Farm_345 Farm_346 Farm_347 Farm_348 Farm_349 Farm_350 Farm_351 Farm_352 Farm_353 Farm_354 Farm_355 Farm_356 Farm_357 Farm_358 Farm_359 Farm_360 Farm_361 Farm_362 Farm_363 Farm_364 Farm_365 Farm_366 Farm_367 Farm_368 Farm_369 Farm_370 Farm_371 Farm_372 Farm_373 Farm_374 Farm_375 Farm_376 Farm_377 Farm_378 Farm_379 Farm_380 Farm_381 Farm_382 Farm_383 Farm_384 Farm_385 Farm_386 Farm_387 Farm_388 Farm_389 Farm_390 Farm_391 Farm_392 Farm_393 Farm_394 Farm_395 Farm_396 Farm_397 Farm_398 Farm_399 Farm_400 Farm_401 Farm_403 Farm_404 Farm_405 Farm_406 Farm_407 Farm_408 Farm_409 Farm_410 Farm_411 Farm_412 Farm_413 Farm_414 Farm_415 Farm_416 Farm_417 Farm_418 Farm_419 Farm_420 Farm_422 Farm_423 Farm_424 Farm_425 Farm_426 Farm_427 Farm_428 Farm_429
N_animal_type_1 both 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 1 0 2 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
N_animal_type_2 both 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
N_animal_type_3 both 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 3 0 0 2 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 1 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
N_animal_type_4 both 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 3 0 0 0 0 0 0 2 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 2 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2 2 0 1 2 2 0 0 0 0 1 0 1 5 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 1 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 1 1 0 0 0 1 1 1 1 1 0 0 2 0 0 0 3 0 0 0 0 0 1 2 1 0 0 0 1 0 0 0 0 0 0 0 5 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0 3 1 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 2
N_animal_type_5 both 0 0 2 1 2 1 1 1 2 1 2 1 0 2 1 2 2 1 3 1 2 1 0 1 0 1 1 2 2 1 1 1 1 1 1 1 0 0 1 0 1 1 1 0 1 1 1 1 2 2 1 1 0 4 1 0 1 0 0 1 2 0 1 2 1 1 1 2 3 2 2 1 0 1 0 0 0 1 1 1 1 2 0 2 1 1 0 1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 1 0 0 0 0 1 1 1 2 0 2 1 1 0 1 0 2 0 4 1 2 2 1 0 2 1 3 4 1 1 0 1 1 2 2 1 1 2 2 0 1 0 0 0 0 3 3 1 2 0 1 2 1 2 0 0 1 1 3 2 1 0 1 0 0 1 0 1 3 1 1 0 0 3 2 2 0 2 0 0 1 1 1 1 1 1 0 1 0 1 2 1 2 0 1 0 0 1 1 0 1 0 1 2 1 3 1 1 2 1 1 1 2 0 0 0 1 1 0 2 2 1 2 0 0 1 1 1 4 1 1 0 1 3 1 1 2 1 2 4 1 1 0 0 1 3 1 0 0 0 0 1 1 1 1 2 0 0 0 2 3 0 2 0 0 1 4 1 0 0 1 1 2 0 0 0 6 1 3 0 0 0 2 0 1 1 1 4 1 2 0 1 1 1 1 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 4 1 0 2 0 1 0 1 0 1 1 1 1 1 2 2 1 1 1 1 3 3 4 0 1 1 1 0 0 2 2 0 0 0 0 0 0 2 1 2 0 1 1 1 2 1 0 0 0 0 0 0 1 1 1 0 1 1 1 0 1 2 0 1 1 1 2 0 1 1 1 0 0 1 2 1 1 0 3 0 0 1 0 1 0 0 0 2 2 1 1 1 1 1 1 0 1 2
N_animal_type_6 both 2 0 0 0 1 1 0 0 0 1 3 0 0 1 1 1 1 1 3 0 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 2 2 1 2 1 5 2 2 0 0 0 0 0 0 0 0 0 1 2 0 1 1 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 1 2 1 0 0 0 0 0 0 0 0 1 0 0 0 0 2 0 0 1 0 2 2 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 3 0 1 0 0 2 1 0 0 0 1 0 1 0 1 0 0 2 0 0 1 0 1 0 0 0 1 1 0 1 1 2 1 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 2 0 1 2 0 0 1 0 2 0 0 2 1 1 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 1 1 1 1 0 0 1 2 2 0 0 0 1 0 0 2 0 1 0 1 0 1 0 1 0 1 1 0 0 0 1 0 0 1 0 0 0 0 1 2 0 0 1 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 3 2 1 1 1 0 0 0 2 0 1 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 3
N_animal_type_7 both 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 2 1 4 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 2 2 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 2 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 1 2 0 0 0 0 1 1 0 0 0 0 1 1 2 0 0 2 0 0 0 0 0 0 0 2 0 1 0 1 0 2 1 1 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 2 0 0 0 0 0 0 0 0 1 0 2 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0 0 2 0 0 0 1 0 2 0 0 1 0 0 0 1 1 1 0 0 0 0 0 1 0 0 0 0 2 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1
N_animal_type_8 both 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 0 0 0 0 0 2 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 2 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
N_animal_type_9 both 0 1 0 0 1 1 1 1 0 1 0 1 0 2 2 0 0 2 1 1 1 1 1 0 1 2 1 0 2 0 0 0 0 1 0 1 0 0 0 0 0 2 0 0 1 1 1 0 0 0 0 0 0 1 1 2 0 2 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 3 1 1 0 0 0 0 0 0 0 2 0 0 1 0 0 0 8 5 1 0 0 0 5 0 1 1 1 0 0 0 0 1 1 1 0 0 0 1 1 0 0 2 2 1 3 1 0 0 0 0 1 0 3 0 3 1 1 1 2 0 0 0 1 0 1 2 1 0 0 1 1 0 0 0 1 1 0 0 0 0 0 1 1 1 0 2 0 0 0 1 0 1 0 0 0 2 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 1 0 4 3 1 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 0 0 0 0 1 1 0 1 3 0 1 1 0 1 0 0 1 1 0 0 2 0 0 0 0 1 0 0 0 1 0 2 1 0 1 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 2 0 1 1 0 1 3 1 0 1 0 0 1 1 1 0 0 0 1 0 0 1 0 1 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 1 1 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 1 1 1 1 0 1
N_animal_type_10 both 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 2 1 0 0 0 0 0 0 0 3 0 0 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 4 0 0 1 0 0 0 1 0 1 2 0 0 0 0 0 0 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
N_animal_type_11 both 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1
N_animal_type_12 both 0 0 0 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 1 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 2
feeding_system both 2 3 3 3 3 2 2 3 3 3 1 2 1 2 1 1 3 2 2 2 2 2 2 3 3 3 2 1 2 2 2 2 2 2 3 1 3 1 3 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 2 3 3 3 2 1 2 1 1 1 1 1 1 2 1 1 2 2 1 2 1 1 1 1 2 2 1 2 1 1 1 1 1 1 1 1 1 1 2 1 2 1 2 1 2 2 2 2 2 1 1 1 3 2 3 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 3 1 3 3 1 3 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 3 1 2 2 1 1 1 1 1 1 1 1 1 3 3 1 1 1 3 3 1 1 1 1 2 3 1 1 1 2 2 1 1 1 1 1 1 3 2 1 1 1 3 2 2 1 1 2 1 1 1 1 2 1 1 1 2 3 2 1 1 1 1 1 2 2 3 2 1 1 1 2 1 1 3 1 1 2 2 2 1 1 1 1 1 3 2 3 2 3 1 1 1 1 1 1 1 2 1 1 2 1 1 1 2 1 1 1 1 2 3 1 2 1 2 2 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 3 2 2 2 1 1 1 1 2 2 3 1 1 2 1 1 1 2 3 3 2 2 2 3 3 3 1 1 1 2 2 3 3 1 1 1 1 1 1 1 1 1 1 2 2 2 2
milk_yield_4 lower 2.826069862825 6.4226802686 3.784476164175 2.0536214164 7.80752996625 4.249876742375 2.68157074575 2.996510750825 10.93054195275 7.8484875 6.199050416075 5.6214215757 4.5827326415 7.5423299325 1.93524349325 2.1199373984 5.12600455475 4.6661698494 1.482048209575 2.064762502275 4.172648540325 1.8738071919 6.53476829185 5.30912037705 4.56136893455 4.0666921788375 4.6285123985625 4.3722167811 5.0916213452875 2.966861387175 5.13402265455 4.844092369825 4.585352913735 5.30912037705 4.264703253375 4.2576226634 2.539818324525 3.380594267625 2.38304186025 5.232325 5.23437287705 1.56150599325 5.10075754625 3.03495328785 6.38531759539167 9.99363835825 0.93690359595 4.05991558245 8.119831162 4.846551129025 5.297369782225 2.9899 5.79293125 5.228436223975 4.215247961275 2.66222462245 4.4479882189 3.86062748385 4.13253965025 2.623330068225 1.68181875 2.4984095892 1.405355393925 2.09393345945 1.56150599325 4.3722167811 2.342258989875 13.3076046050833 1.8738071919 2.3028373631875 8.222225 1.865451899675 5.48979333882 3.43531318515 6.196260025925 3.245260640425 3.380594267625 8.92027998301667 13.0736449325 1.7281877983 0.8145741415 5.008628743275 3.1753455333125
milk_yield_4 upper 4.969984931175 11.2950584034 6.655458081825 3.6115411116 13.73048373375 7.473921167625 4.71586579425 5.269725803175 19.22267722725 13.8025125 10.901778317925 9.8859482883 8.0592884385 13.2640974675 3.40335924675 3.7281657696 9.01469766525 8.2060228386 2.606360644425 3.631134055725 7.338106053675 3.2953160961 11.49217872015 9.33672893895 8.02171778145 7.1517690041625 8.1397976664375 7.6890708909 8.9542306417125 5.217583818825 9.02879846145 8.518921064175 8.063896503465 9.33672893895 7.499995376625 7.4875433046 4.466577053475 5.945183022375 4.19086671975 9.201675 9.20527643895 2.74609674675 8.97029775375 5.33733164415 11.229351633275 17.57501918175 1.64765804805 7.13985154155 14.279703078 8.523245088975 9.316064099775 5.2581 10.18756875 9.194836118025 7.413022276725 4.68184330155 7.8223241091 6.78937936815 7.26756972975 4.613442533775 2.95768125 4.3937547948 2.471487072075 3.68243470455 2.74609674675 7.6890708909 4.119145120125 23.40302878825 3.2953160961 4.0498174318125 14.459775 3.280622306325 9.65446414758 6.04141284285 10.896871080075 5.707182505575 5.945183022375 15.68738893565 22.9915824675 3.0392268177 1.4325269385 8.808278134725 5.5842283516875
milk_yield_4 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
milk_yield_5 lower 3.8373469091625 4.169698822725 4.4510409789375 3.380594267625 6.4226802686 5.055305968775 2.605069001675 1.0167000844 8.90058416225 1.62969614085 2.5061615396875 7.548048892 2.0862333870625 2.4984095892 3.979987233175 4.9856218376 3.1496335051 7.0267769678125 6.53476829185 4.471052225575 1.829250770475 2.17352909245 3.332470536625 1.6693531300875 3.17676875 1.7412176984 5.6214215757 6.53476829185 4.05991558245 6.757993150675 4.734627331325 4.04562920515 6.1876979144 2.0190180335 4.73962239935 2.70065660275 1.784746671575 3.0480767985 7.182927568225 3.68481802595 6.9582240403625 4.958803377825 1.806873549775 2.4750500888375 2.458211640125 5.729476356525 8.5959625 9.8021524385 9.50056069625 6.8706263699375 6.603491911125 3.454352141175 3.0480767985 4.5851119700625 3.40695084609167 7.415030869125 4.6202951119 4.281786845975 5.182979424325 6.558325170925 6.761188534525 5.258243695625 9.59269285525 2.6032003486125 4.850258351275 7.182927568225 2.4984095892 6.020757534475 7.51710556575 4.393874869225 4.04562920515 3.5386088028 9.99363835825 7.2117848237 8.6348686245 7.548048892 0.780633618125 1.8738071919 10.39116890825 10.92864316325 8.00088938025 3.8192920105 8.2536197881 12.720224006 3.268506244375 5.295067447575 3.8762662786875 3.3502472839625 6.741820116025 4.05700278475 8.0442537835 7.68615835425 3.663058521925 4.779961288425 5.51087738388333 7.3159959869375 3.454352141175 2.134463140975 2.47940168855 3.71910253355 3.873607372725 8.671859135875 7.886557163 4.05991558245 7.266853898825 3.77932572865 3.8871201308 9.98933984533333 5.88821947561667 0.493780838775 4.33101128285 3.520898151175 1.9845421646875 7.47475 2.6555841266875 8.67566171375 5.3614828887 1.517138197975 4.4092528924375 5.6214215757 7.3238728455 9.14220694225 1.3540330637 3.48906441955833 6.196260025925 3.6405291491 3.5394135847 3.903764983125 4.05991558245 4.1136548111 4.169698822725 3.205373901225 4.074991965425 1.2878319565 1.464389509275 7.77446913975 6.196260025925 0.750275791 2.5831277687875 4.3722167811 0.9706280892625 5.295067447575 10.36595884575 3.43531318515 1.733442793325 2.028805399375 2.4456472230125 5.3500779108 7.0963070528 5.409399667 5.30912037705 3.9439988200625 4.281786845975 3.042086000975 5.182979424325 6.590812303475 10.618240757 8.89983776025 3.21246223275 2.5822628398 5.7340547946 1.9390341046375 5.933722773625 3.32031176245 3.945522776225 7.883624598175 4.0193162422 3.43531318515 3.66057962295 6.592456449775 1.239700844275 7.99864518625 3.519788099325 6.246023972275 6.3758299511625 14.9123822348125 9.75263168325 1.46612108695 4.057610799475 9.13727828108333 6.985364581025 2.224379417225 3.196294745075 5.30912037705 4.169698822725 5.058940258925 7.2678288371375 2.95754834360833 1.5544368136875 7.54933655 1.63676211718125 5.859995956175 4.169698822725 8.0364692265 5.9374904090375 4.2549032772125 7.110830225975 7.806355800475 4.50820708865 4.393874869225 2.5914897118 1.319144650675 2.14126461591875 0.91563711295 2.1010776547625 8.6348686245 6.084172002675 12.05401178375 1.521290093175 3.3402663855 5.5339670007 8.67566171375 1.1787611121 4.007922490925 5.42990381231875 1.245090153525 4.6836241326875 2.8179099552 1.3679237505 1.64542057595 2.70934089055 5.152969777725 4.471052225575 2.947013826325 3.5709784016125 7.41653926425 5.74566373675 6.246023972275 4.9968191784 2.641801455825 5.11924452425 4.17416850596667 4.5059450031 5.232325 2.967279871675 4.281786845975 2.7265130342875 8.891471108 1.9161347454625 2.028805399375 1.5020526742 1.46612108695 6.53476829185 15.6150599325 3.9180641563125 3.3789965757 5.6214215757 4.48485 4.48485 5.1241055695 2.47940168855 4.8444711584 2.242425 2.538136975575 7.9544346065 3.86062748385 8.44749143925 7.986389657925 4.989967443375 2.94276528355 2.28379794425 4.50820708865 2.084849411 3.155296122675 3.380594267625 8.97605924325 3.7595645124 5.230846863475 9.805345904 4.691178794725 4.3679657987 4.393874869225 3.192802847825 3.923329189325 5.026015332225 6.246023972275 4.989967443375 1.497613221525
milk_yield_5 upper 6.7484376678375 7.332918619275 7.8276927560625 5.945183022375 11.2950584034 8.890365669225 4.581328244325 1.7879898036 15.65275145775 2.86601735115 4.4073875353125 13.274154948 3.6688931979375 4.3937547948 6.999287892825 8.7678177144 5.5390106469 12.3574353571875 11.49217872015 7.862884948425 3.216958251525 3.82241323155 5.860551633375 2.9357589529125 5.58673125 3.0621414696 9.8859482883 11.49217872015 7.13985154155 11.884746575325 8.326413582675 7.11472722285 10.8818135736 3.5506868865 8.33519801265 4.74943057725 3.138692422425 5.3604109215 12.632045033775 6.48019721805 12.2368767606375 8.720654216175 3.177605208225 4.3526742941625 4.323061849875 10.075975661475 15.1170375 17.2382680815 16.70788260375 12.0828256850625 11.613037498875 6.074895144825 5.3604109215 8.0634727749375 5.991534246575 13.040226700875 8.1253465761 7.530038936025 9.114894849675 11.533606335075 11.890366043475 9.247256154375 16.86990812475 4.5780419923875 8.529764686725 12.632045033775 4.3937547948 10.588228767525 13.21973737425 7.727159252775 7.11472722285 6.2230706532 17.57501918175 12.6827940003 15.1854586155 13.274154948 1.372838431875 3.2953160961 18.27412463175 19.21933797675 14.07052959975 6.7166859495 14.5149865239 22.370049114 5.748062705625 9.312015166425 6.8168820763125 5.8918141890375 11.856304341975 7.13472903525 14.1467911365 13.51703710575 6.441930504075 8.406138817575 9.69154298545 12.8660619080625 6.074895144825 3.753711041025 4.36032710745 6.54049066245 6.812206069275 15.250510894125 13.869462597 7.13985154155 12.779639615175 6.64640041935 6.8359698852 17.567459728 10.35514459505 0.868373199225 7.61660604915 6.191924334825 3.4900569103125 13.14525 4.6701651883125 15.25719818625 9.4288147353 2.668070624025 7.7542033625625 9.8859482883 12.8799143145 16.07767427775 2.3812305603 6.135940875775 10.896871080075 6.4023098829 6.2244859593 6.865241866875 7.13985154155 7.2343584609 7.332918619275 5.637036860775 7.166365180575 2.2648079235 2.575305688725 13.67234228025 10.896871080075 1.319450529 4.5427419382125 7.6890708909 1.7069666397375 9.312015166425 18.22978969425 6.04141284285 3.048468360675 3.567899150625 4.3009658059875 9.4087577052 12.4797124032 9.513082173 9.33672893895 6.9359979249375 7.530038936025 5.349875381025 9.114894849675 11.590738878525 18.673457883 15.65143881975 5.64950254725 4.5412208562 10.0840273974 3.4100254943625 10.435167636375 5.83916896155 6.938677985775 13.864305327825 7.0684527018 6.04141284285 6.43757106105 11.593630308225 2.180163553725 14.06658291375 6.189972174675 10.984386985725 11.2126664658375 26.2252239301875 17.15117985675 2.57835087705 7.135798302525 16.06900663225 12.284606676975 3.911839664775 5.621070068925 9.33672893895 7.332918619275 8.896757007075 12.7813541618625 5.201205707725 2.7336647413125 13.27641945 2.87844372331875 10.305510129825 7.332918619275 14.1331010535 10.4417934779625 7.4827609357875 12.505253156025 13.728418821525 7.92822625935 7.727159252775 4.5574474242 2.319875075325 3.76567225558125 1.61025837105 3.6949986342375 15.1854586155 10.699750763325 21.19843451625 2.675372232825 5.8742615745 9.7321488633 15.25719818625 2.0729936799 7.048415415075 9.54914118718125 2.189641304475 8.2367183023125 4.9556347488 2.4056590095 2.89367066805 4.76470294545 9.062119264275 7.862884948425 5.182679487675 6.2799964993875 13.04287939575 10.10444312325 10.984386985725 8.7875095896 4.645926698175 9.00280933575 7.3407790967 7.9242481089 9.201675 5.218319774325 7.530038936025 4.7949022327125 15.636725052 3.3697542075375 3.567899150625 2.6415409098 2.57835087705 11.49217872015 27.4609674675 6.8903886886875 5.9423732883 9.8859482883 7.88715 7.88715 9.0113580705 4.36032710745 8.5195872096 3.943575 4.463620198425 13.9888332735 6.78937936815 14.85593322075 14.045030088075 8.775459986625 5.17520791245 4.01633431575 7.92822625935 3.666459309 5.548969043325 5.945183022375 15.78548349675 6.6116479356 9.199075518525 17.243884176 8.250004087275 7.6815950253 7.727159252775 5.614929146175 6.899647884675 8.838854549775 10.984386985725 8.775459986625 2.633733596475
milk_yield_5 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
milk_yield_6 lower 5.471348045275 5.258243695625 6.515181077775 1.21530016815 8.11983116610833 7.182927568225 4.515568150675 2.0136630037 4.3722167811 0.7989502001 6.13587228095833 6.053009964225 5.258243695625 2.028805399375 6.558325170925 1.8046293536 2.7654600669125 6.8585543202625 5.22648855135 2.5914897118 6.4021745716 5.9798 5.337115850835 7.6132553764125 11.391314212875 2.39396787705 4.299878433625 3.101290966925 3.02116903395 5.955474039475 5.6214215757 2.767713709475 1.829250770475 8.432132365 9.99363835825 13.29371262875 3.40101125 3.3262262120375 11.058411254 5.5368621142 9.57839173 8.9697 5.6060625 2.820154151375 4.048927255525 9.48581189896667 2.143137619525 3.0209701592 5.454221900975 5.153124937875 2.376275048825 2.7103407105 4.88537907025 5.232325 10.51648739125 4.108475858425 6.084172002675 5.409399667 3.492682290875 5.528653724375 7.4952287705 5.7974818778 3.7476143838 3.942162670925 7.9544346065 7.203406335825 3.934790048775 2.6939818152125 4.68451797975 7.7579148276 2.81071078785 2.1285827141875 4.9277033388375 7.47475 3.43531318515 4.9968191784 5.7775721743 11.86744554725 11.2428431485 3.1230119865 3.61245452025 7.1677786751 2.03542362495 3.9134923973 3.082503448875 7.0681560191 14.9495 7.72125496625 4.05991558245 4.4510409789375 4.3149996444375 3.1511632261 2.6103525714875 7.182927568225 3.4563755966 2.445445568975 3.077405733175 2.147118427775 4.702946207375 5.6214215757 4.4507292612 5.61291547115 2.7430284317 4.48700027025 5.182979424325 5.30912037705 0.92945493725 4.461965668625 3.1230119865 4.18245300895 3.942162670925 5.70267459205 3.4117626027 1.867519206725 2.947013826325 2.54292513005 3.5295060421 5.30912037705 1.910446250825 4.9968191784 7.182927568225 1.5051893838 2.755659048925 2.23533493741667 3.661561170725 0.79252828785 4.281786845975 12.504608078 2.3361982983625 2.70934089055 5.232325 5.6214215757 2.5526912237 0.8918503081 5.6214215757 6.3535375 5.7775721743 2.22991548000833
milk_yield_6 upper 9.622025872725 9.247256154375 11.457732240225 2.13725201985 14.279703085225 12.632045033775 7.941171575325 3.5412694203 7.6890708909 1.4050503519 10.790671942375 10.644948557775 9.247256154375 3.567899150625 11.533606335075 3.1736585184 4.8633952900875 12.0615955287375 9.19141090065 4.5574474242 11.2589966604 10.5162 9.385962358365 13.3888284205875 20.033000857125 4.21008143895 7.561855176375 5.453994459075 5.31309037005 10.473419862525 9.8859482883 4.867358592525 3.216958251525 14.828922435 17.57501918175 23.37859807125 5.98108875 5.8495702349625 19.447550826 9.7372402698 16.84475787 15.7743 9.8589375 4.959581438625 7.120527242475 16.6819450637 3.768966158475 5.3127406248 9.591907481025 9.062392132125 4.178966465175 4.7664612495 8.59152870975 9.201675 18.49451230875 7.225250647575 10.699750763325 9.513082173 6.142303339125 9.722804825625 13.1812643895 10.1955715782 6.5906321922 6.932768835075 13.9888332735 12.668059418175 6.919803189225 4.7376921577875 8.23829024025 13.6432295244 4.94297414415 3.7433696008125 8.6659610441625 13.14525 6.04141284285 8.7875095896 10.1605579617 20.87033527275 19.7718965715 5.4921934935 6.35293725975 12.6054038769 3.57953809905 6.8823486987 5.420954341125 12.4302054129 26.2905 13.57875873375 7.13985154155 7.8276927560625 7.5884476505625 5.5417008459 4.5906200395125 12.632045033775 6.0784536354 4.300611173025 5.411989392825 3.775966890225 8.270698502625 9.8859482883 7.8271445628 9.87098927685 4.8239465523 7.89093150975 9.114894849675 9.33672893895 1.63455868275 7.846905141375 5.4921934935 7.35534839505 6.932768835075 10.02884152395 5.9999963013 3.284257915275 5.182679487675 4.47204074595 6.2070623499 9.33672893895 3.359750303175 8.7875095896 12.632045033775 2.6470571922 4.846159017075 3.93110626925 6.439297231275 1.39375664415 7.530038936025 21.990862482 4.1084866626375 4.76470294545 9.201675 9.8859482883 4.4892156003 1.5684264039 9.8859482883 11.1734625 10.1605579617 3.921575499325
milk_yield_6 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
Cp_rate_4 lower
Cp_rate_4 upper
Cp_rate_4 distribution
Cp_rate_5 lower
Cp_rate_5 upper
Cp_rate_5 distribution
Cp_rate_6 lower
Cp_rate_6 upper
Cp_rate_6 distribution
feed_prod_CO2 lower 331.184971095 45.133148075 566.3995845 470.46152097375 1182.00021984643 269.7625261275 221.746107575 180.3104770925 215.74048304 47.124724665 1619.94680895 353.1609927 636.347764875 285.60514185 188.18787803125 463.295739725 557.70561879 107.39145537 353.795374058333 205.0853106 1125.06879889091 505.824049725 376.482458925 287.2287823875 115.7195333625 480.199254775 489.9038701 295.571760475 238.69117833 111.163082325 372.58146465 1361.641556625 291.7163923125 400.68005835 912.2124669 521.3961152625 183.185970525 1596.671548125 2362.96138125 658.50883510125 1978.17146925 438.181289725 657.110004825 706.4307762 306.3222104625 95.23853010375 85.7582934925 606.030680475 1004.416333125 1048.23661425 660.8741193 552.34185195 1410.624681825 518.534299465909 78.8871701775 223.729306425 202.857262425 396.77284790625 355.474324095 318.36475275 637.118123308929 758.92981695 830.80610975 618.792585675 1302.09833175 1396.68268875 1161.66945075 1325.623914375 621.5288507 515.3475348375 903.8513821125 1168.242768 784.46300685 1071.65347925 1636.336480125 832.7693655 623.002074375 1086.37102566 591.2651619375 737.00905815 282.944490909375 1385.9253989 552.3941231625 933.4779515625 288.342451561875 477.6709603875 313.434833175 231.4826089875 1096.897232475 417.24882855 515.391147375 1363.34610275 979.53714470625 709.9601368875 1004.580879255 419.8667931 405.7890725 720.043494225 112.255401525 138.206260955 66.4963597725 819.44189445 154.841742405 311.8910413875 349.1921323725 291.8935153125 81.189258375 83.8100086175 127.28276942625 489.077630425 462.9194262 331.233060774375 575.48907525 1231.37069191875 563.81972007625 361.285006292143 1220.668935 1315.103631 515.47392255 234.10156215 302.1182847375 343.3294324275 473.34933506625 1445.925024 2231.455147 538.042000659 123.0474409375 949.7441584125 350.223593199 324.79517655 940.19853645 425.22357978075 349.354235325 778.77374775 1285.8053836875 952.67970585 1199.7611907 1369.865711025 1659.0263969325 1780.591548 1915.26922303125 867.211457325 52.536350085 7.6069583875 127.3570601325 86.853056515 249.79853838 143.4432540375 303.3130207125 493.13594341875 1469.40168714 433.0990178325 150.10855328625 1163.52075375 169.0224357 139.609835570455 746.744690775 522.375852 1380.6223783875 355.6843372125 287.736852375 190.4475783 161.8400738 1601.5707015 2170.490441175 444.67161576 870.85420275 1170.644431125 781.07875275 846.4784110875 270.057208375 125.8169126325 451.865788575 106.1997366375 745.011298125 872.711914125 452.1556114875 873.9855819375 786.848487025 1092.168066375 1053.082658625 1911.95584779 841.460467125 774.398374 425.9943639825 796.227198075 1491.62738325 1127.16035793 1022.85485475 1372.9690411875 1164.5684667 1632.5951966625 1473.91577473125 1947.89956875 90.1068105375 151.756209993 154.39072069245 338.4441912 343.376059875 352.16687295 1532.27062033125 1504.22199755 684.835852875 566.802753225 466.172613375 894.47141025 134.316288529 303.5655937645 789.49499995 562.61442965625 1612.4735814 153.71706614625 964.81319925 974.48336105 924.2950824 671.501069525 454.491485892 517.370629575 393.3919245375 858.182761575 1305.66227113125 1139.24953392 966.3423046875 43.88182512 1105.28096325 414.03606735 900.346222275 231.38486115 443.3917322 237.81143578875 235.310495625 697.0618696125 1049.55975015937 447.2160168 355.70307825 359.9726216625 651.84876795 1061.61973818 681.3787384875 1078.01166925 695.9118286 238.493822205 1464.185981625 1127.33029767094 791.9021649 1137.42468 461.6743311 1323.613750875 712.0210086375 640.643769032143 294.987838635 283.752806475 414.59708675625 811.5177489375 648.1342191 379.565490525 239.77228801875 432.417971409375 854.8820767125 1995.057891375 256.080262175 190.002861525 811.09109625 678.7387021 686.3208839375 206.883089025 826.89208275 866.1170535 873.33794775 108.98741729625 524.884049537812 208.776617086875 551.4629180625 58.4515096725 497.5471683 390.44072698875 387.718809 396.62859102 54.9072081525 319.78195515 175.330860456818 1328.47707225 607.5680754375 141.0410877 638.0879556375 251.6923866 623.98175169375 476.923572225 309.563222715 410.015538675 885.713215925 859.0329980625 366.4939713525 573.67211541 122.459287905 423.21653244375 308.9153216 795.7313945625 823.7974499475 407.2160575425 1150.17374025 1605.38616 279.9452491875 337.3708224825 237.0292762125 784.45451355 799.900846125 385.8074146875 391.1315522 162.87839625 421.585977555 682.0357177 407.905951575 219.65339742 430.468979475 282.534897125 388.835054914286 264.421397045 315.1862088 384.581756630625 456.9023123625 217.84250128125 322.761763875 148.5090135275 143.5290021 339.2775799125 136.30346715 260.50228600875 312.6481227375 230.63129604 605.014421 964.77708165 1467.953922 282.55015655 228.26394770625 990.37868925 1834.0753425 1240.81842525 1650.80568625 522.494913225 165.4195330575 94.53219765 179.31836143875 206.02596255 184.9645044 2.606505474 162.59738689 368.39184105 407.7373768875 302.678074875 459.45447241125 303.34542525 322.895338325 324.24429699 740.000058325 427.043847075 1042.6094212 803.50864215 874.213281575 856.984759608333 357.15840890625 403.1562556875 182.361238575 267.041411375 188.6022609 706.982967675 222.83988735 914.1111551625 549.23474965 1017.2233305 265.3756534875 1473.9824565 396.674810575 219.109250625 290.97672045 112.474092975 798.6555375 230.82554842875 154.114733325 2044.93177791 473.295572625 450.226236125 739.4944125375 734.2872849375 544.3810809 504.304106175 306.88398315 443.186385375 217.0479219 45.1331480625 45.1331480625 45.1331480625 45.1331480625 131.450563725 971.8097475 3550.991472 238.0666441875 146.923828635 203.323336785 35.834667495 88.750233 500.7109789725 141.593706825 494.6392201125 457.7628552375 198.164479975 348.346914375 406.48811951625 148.985693325 815.4782313375 236.2676605375 315.8898776625 548.08092915 1391.56630870625
feed_prod_CO2 upper 551.974951825 75.2219134583333 943.9993075 784.10253495625 1970.00036641071 449.6042102125 369.576845958333 300.517461820833 359.567471733333 78.541207775 2699.91134825 588.6016545 1060.579608125 476.00856975 313.646463385417 772.159566208333 929.50936465 178.98575895 589.658956763889 341.808851 1875.11466481818 843.040082875 627.470764875 478.7146373125 192.8658889375 800.332091291667 816.506450166667 492.619600791667 397.81863055 185.271803875 620.96910775 2269.402594375 486.1939871875 667.80009725 1520.3541115 868.9935254375 305.309950875 2661.119246875 3938.26896875 1097.51472516875 3296.95244875 730.302149541667 1095.183341375 1177.384627 510.5370174375 158.73088350625 142.930489154167 1010.051134125 1674.027221875 1747.06102375 1101.4568655 920.56975325 2351.041136375 864.223832443182 131.4786169625 372.882177375 338.095437375 661.28807984375 592.457206825 530.60792125 1061.86353884821 1264.88302825 1384.67684958333 1031.320976125 2170.16388625 2327.80448125 1936.11575125 2209.373190625 1035.88141783333 858.9125580625 1506.4189701875 1947.07128 1307.43834475 1786.08913208333 2727.227466875 1387.9489425 1038.336790625 1810.6183761 985.4419365625 1228.34843025 471.574151515625 2309.87566483333 920.6568719375 1555.7965859375 480.570752603125 796.1182673125 522.391388625 385.8043483125 1828.162054125 695.41471425 858.985245625 2272.24350458333 1632.56190784375 1183.2668948125 1674.301465425 699.7779885 676.315120833333 1200.072490375 187.092335875 230.343768258333 110.8272662875 1365.73649075 258.069570675 519.8184023125 581.9868872875 486.4891921875 135.315430625 139.683347695833 212.13794904375 815.129384041667 771.532377 552.055101290625 959.14845875 2052.28448653125 939.699533460417 602.141677153571 2034.448225 2191.839385 859.12320425 390.16927025 503.5304745625 572.2157207125 788.91555844375 2409.87504 3719.09191166667 896.736667765 205.079068229167 1582.9069306875 583.705988665 541.32529425 1566.99756075 708.70596630125 582.257058875 1297.95624625 2143.0089728125 1587.79950975 1999.6019845 2283.109518375 2765.0439948875 2967.65258 3192.11537171875 1445.352428875 87.560583475 12.6782639791667 212.2617668875 144.755094191667 416.3308973 239.0720900625 505.5217011875 821.89323903125 2449.0028119 721.8316963875 250.18092214375 1939.20125625 281.7040595 232.683059284091 1244.574484625 870.62642 2301.0372973125 592.8072286875 479.561420625 317.4126305 269.733456333333 2669.2845025 3617.484068625 741.1193596 1451.42367125 1951.074051875 1301.79792125 1410.7973518125 450.095347291667 209.6948543875 753.109647625 176.9995610625 1241.685496875 1454.519856875 753.5926858125 1456.6426365625 1311.41414504167 1820.280110625 1755.137764375 3186.59307965 1402.434111875 1290.66395666667 709.9906066375 1327.045330125 2486.04563875 1878.60059655 1704.75809125 2288.2817353125 1940.9474445 2720.9919944375 2456.52629121875 3246.49928125 150.1780175625 252.927016655 257.31786782075 564.073652 572.293433125 586.94478825 2553.78436721875 2507.03666258333 1141.393088125 944.671255375 776.954355625 1490.78568375 223.860480881667 505.942656274167 1315.82499991667 937.69071609375 2687.455969 256.19511024375 1608.02199875 1624.13893508333 1540.491804 1119.16844920833 757.48580982 862.284382625 655.6532075625 1430.304602625 2176.10378521875 1898.7492232 1610.5705078125 73.1363752 1842.13493875 690.06011225 1500.577037125 385.64143525 738.986220333333 396.35239298125 392.184159375 1161.7697826875 1749.26625026563 745.360028 592.83846375 599.9543694375 1086.41461325 1769.3662303 1135.6312308125 1796.68611541667 1159.85304766667 397.489703675 2440.309969375 1878.88382945156 1319.8369415 1895.7078 769.4572185 2206.022918125 1186.7016810625 1067.73961505357 491.646397725 472.921344125 690.99514459375 1352.5295815625 1080.2236985 632.609150875 399.62048003125 720.696619015625 1424.8034611875 3325.096485625 426.800436958333 316.671435875 1351.81849375 1131.23117016667 1143.86813989583 344.805148375 1378.15347125 1443.5284225 1455.56324625 181.64569549375 874.806749229688 347.961028478125 919.1048634375 97.4191827875 829.2452805 650.73454498125 646.198015 661.0476517 91.5120135875 532.96992525 292.218100761364 2214.12845375 1012.6134590625 235.0684795 1063.4799260625 419.487311 1039.96958615625 794.872620375 515.938704525 683.359231125 1476.18869320833 1431.7216634375 610.8232855875 956.12019235 204.098813175 705.36088740625 514.858869333333 1326.2189909375 1372.9957499125 678.6934292375 1916.95623375 2675.6436 466.5754153125 562.2847041375 395.0487936875 1307.42418925 1333.168076875 643.0123578125 651.885920333333 271.46399375 702.643295925 1136.72619616667 679.843252625 366.0889957 717.448299125 470.891495208333 648.058424857143 440.702328408333 525.310348 640.969594384375 761.5038539375 363.07083546875 537.936273125 247.515022545833 239.2150035 565.4626331875 227.17244525 434.17047668125 521.0802045625 384.3854934 1008.35736833333 1607.96180275 2446.58987 470.916927583333 380.43991284375 1650.63114875 3056.7922375 2068.03070875 2751.34281041667 870.824855375 275.6992217625 157.55366275 298.86393573125 343.37660425 308.274174 4.34417579 270.995644816667 613.98640175 679.5622948125 504.463458125 765.75745401875 505.57570875 538.158897208333 540.40716165 1233.33343054167 711.739745125 1737.68236866667 1339.18107025 1457.02213595833 1428.30793268056 595.26401484375 671.9270928125 303.935397625 445.069018958333 314.3371015 1178.304946125 371.39981225 1523.5185919375 915.391249416667 1695.3722175 442.2927558125 2456.6374275 661.124684291667 365.182084375 484.96120075 187.456821625 1331.0925625 384.70924738125 256.857888875 3408.21962985 788.825954375 750.377060208333 1232.4906875625 1223.8121415625 907.3018015 840.506843625 511.47330525 738.643975625 361.7465365 75.2219134375 75.2219134375 75.2219134375 75.2219134375 219.084272875 1619.6829125 5918.31912 396.7777403125 244.873047725 338.872227975 59.724445825 147.917055 834.5182982875 235.989511375 824.3987001875 762.9380920625 330.274133291667 580.578190625 677.48019919375 248.309488875 1359.1303855625 393.779434229167 526.4831294375 913.46821525 2319.27718117708
feed_prod_CO2 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
feed_trans_CO2 lower 0.360618571 0.14424742825 4.038927994 2.853845215125 57.8117651528571 2.17152482775 0.44081583525 0.5769897135 1.153979427 0 1.639011405 0.06491134275 0.15867217125 0 0.15025773775 0.0901546425 1.7309691405 0.2307958854 0.036061857 0.0216371142 0.926224948840909 0.265054649625 0.265054649625 0.151459799625 0 3.233938362 0 0.28849485675 0.36061857075 0 0.7211402173125 3.1600356445 0 1.15397942675 2.277490788 16.52659944 0.023945073 21.70924918875 0.6923876565 0.3894680565 3.24556713825 1.1704761975 0.322198810875 0.173096914125 0.1298226855 0 0.01442474275 0.180309285375 1.817517597 1.817517597 1.1684041695 0.15867217125 2.190757818375 0 0 0 0.0112693303125 0.237557483625 0.08654845695 0.1298226855 17.77957201875 0.14955573375 0.43274228475 0.0753918200625 3.8042568045 6.812267805 0 4.628566269 1.1163223 0.27887836125 1.9591814895 0.26483827875 0.432742285125 0.1373956755 3.79204277325 0 1.7309691405 0.77572392255 0.596286999375 0.11212307775 8.265296908125 0.40389279925 0.311574445125 1.321476331625 6.407454298125 1.4280495406875 0.6491134275 0.0180309285 0.940489439625 0.254236092375 0.17309691375 7.8421944425 2.423356796625 2.00070219646875 2.69745674805 0.519290742 0.461591771 0.43274228475 7.8210882225 0 0 0 0.370921959 0.139018459125 2.0942923505625 1.119341422125 0 0 0 9.190918411 2.0335650545 5.273498941875 0 3.1989888279375 4.475594317625 0.197825044607143 0.517181123625 0.28788180525 0.3155412495 0 4.10274682575 0 0.710529820375 0.398122902 0.61818260275 2.8158369402 0 0.238008256875 0 0.207458712 0.405695892375 2.18882735235 0.45077321325 0 2.9490395026875 4.688762660625 8.0927134725 32.77123260375 97.79594418 21.745299824625 14.38279838925 21.2013736265625 0 0 8.176414785375 1.937637926 0.0600838344 0.25657290075 32.67204252375 109.78839804375 26.4007542465 0 1.593714106 1.147805637 0.43245379 1.69126938688636 7.299003943125 0.654901355625 0.068562605625 0 0 0 0 0.476021453625 0 8.94210552075 0.42192372825 0.2434175355 0.53371548525 2.25237390309375 2.575026957 21.342833355 0 0 1.153979427 0.21637114275 0.05407549275 0.21637114275 0.2163711425 0 0 0.34619382825 0 0 2.6956238175 0 0.21637114275 4.0334648919 2.99020074825 8.816316704625 3.0291959955 2.287410393375 0.272015268 0.34619382825 0 0 0 0.17309691375 0.115397943 0.0540927855 0.45516806175 0.38280283225 0 0.64807484625 0 0 0.420721666 3.8465980895 2.677548036 1.106937541875 39.720153982875 1.21440053025 1.081855712625 4.444893004 0 1.17550889375 0.1469386116 0.15867217125 0 1.79847693675 10.59182493 6.17640605865 0.993510333375 0 2.73493124175 5.531239890375 1.21550317039286 1.68460389525 0.0901546425 0.013523196375 0.0300515475 0.97367014125 2.5500795620625 0.27046392825 0.27046392825 0.27046392825 0.658421465625 3.62762103405 0.28849485675 1.18616598 0.54141944025 3.899990085 1.009603447125 1.33171879771875 1.059350556375 0.0167839395 0.54525527925 3.953286235125 0.34619382825 0 4.36008305225 0.5529484755 1.7528275576875 3.323956067625 0.259645371 4.26121974 23.7132622875 0.562564970625 1.529005645875 0.352892441625 0.5787928065 0 0.81139178475 0.270463928 0.051929074375 1.2740209515 0.43274228475 0 0 0.0108185570625 1.4374749106875 0.6428564263125 2.077162968375 0 1.190041284 0.090154642875 4.37939390175 0.69947037135 0 0.45077321325 0.245876298136364 2.045783592 0.0540927855 0.27046392825 2.596453710375 0.86548457025 1.298226855375 2.04099346425 0.519290742 0.259645371 0 1.557872226 0.261664835 1.9906145115 0 1.7309691403125 0 0 0 0 0 0.917413644 0.146050521375 0 0.587754447 2.120437197 1.7309691405 0.432742285125 0.261664835 0 0 0.209158771 1.4693861175 0.73469305875 0 0 0.488180235214286 0 0.02596453725 1.358975475375 0.36419590725 0.1298226855 0 0 0 0.9231835415625 0 0.0540927855 0 0.03894680595 0.2148986165 0.7343997196875 0.57662909475 1.29162315675 0 6.988021057875 1.341501084 0.23368083375 1.291014483875 0.5534293005 0.04111051725 0 0.205714056375 0.205714056375 0.072123714 0 0.0288494855 4.28072596175 0.781300222125 0.7309359825 4.0191235425 1.539351162375 2.01007892325 0.1904066055 0.72989198775 0.759182827375 1.58672171175 8.0816236460625 23.007464825 7.83672595941667 0 0.2622418245 0.0395836695 0.35427378 0 12.13898517375 4.017301645 0.8776554470625 0 2.2304619225 0.78902441775 0.458706822 0.37023506625 0 0.043274228625 0 11.7580134975 5.323992608 0.024233568 16.5247168617 0.8632643439375 0.225025988 10.0295398411875 2.531542367625 0.30291959925 0.30291959925 0.2380082565 0.22502598825 0.28849485675 0.28849485675 0.28849485675 0.28849485675 0.28849485675 0.072123714 1.0097319985 27.2840097575 0.43274228475 0.1682886665 0 0 0 0.6491134275 0 0 0.259645371 0 0.02596453725 0 0.61968695225 1.038581484 0.550653515 0 0.30291959925 2.991011502125
feed_trans_CO2 upper 0.601030951666667 0.240412380416667 6.73154665666667 4.756408691875 96.3529419214286 3.61920804625 0.73469305875 0.9616495225 1.923299045 0 2.731685675 0.10818557125 0.26445361875 0 0.250429562916667 0.1502577375 2.8849485675 0.384659809 0.060103095 0.036061857 1.54370824806818 0.441757749375 0.441757749375 0.252432999375 0 5.38989727 0 0.48082476125 0.60103095125 0 1.2019003621875 5.26672607416667 0 1.92329904458333 3.79581798 27.5443324 0.039908455 36.18208198125 1.1539794275 0.6491134275 5.40927856375 1.9507936625 0.536998018125 0.288494856875 0.2163711425 0 0.0240412379166667 0.300515475625 3.029195995 3.029195995 1.9473402825 0.26445361875 3.651263030625 0 0 0 0.0187822171875 0.395929139375 0.14424742825 0.2163711425 29.63262003125 0.24925955625 0.72123714125 0.1256530334375 6.3404280075 11.353779675 0 7.714277115 1.86053716666667 0.46479726875 3.2653024825 0.44139713125 0.721237141875 0.2289927925 6.32007128875 0 2.8849485675 1.29287320425 0.993811665625 0.18687179625 13.775494846875 0.673154665416667 0.519290741875 2.20246055270833 10.679090496875 2.3800825678125 1.0818557125 0.0300515475 1.567482399375 0.423726820625 0.28849485625 13.0703240708333 4.038927994375 3.33450366078125 4.49576124675 0.86548457 0.769319618333333 0.72123714125 13.0351470375 0 0 0 0.618203265 0.231697431875 3.4904872509375 1.865569036875 0 0 0 15.3181973516667 3.38927509083333 8.789164903125 0 5.3316480465625 7.45932386270833 0.329708407678571 0.861968539375 0.47980300875 0.5259020825 0 6.83791137625 0 1.18421636729167 0.66353817 1.03030433791667 4.693061567 0 0.396680428125 0 0.34576452 0.676159820625 3.64804558725 0.75128868875 0 4.9150658378125 7.814604434375 13.4878557875 54.61872100625 162.9932403 36.242166374375 23.97133064875 35.3356227109375 0 0 13.627357975625 3.22939654333333 0.100139724 0.42762150125 54.45340420625 182.98066340625 44.0012570775 0 2.65619017666667 1.913009395 0.720756316666667 2.81878231147727 12.165006571875 1.091502259375 0.114271009375 0 0 0 0 0.793369089375 0 14.90350920125 0.70320621375 0.4056958925 0.88952580875 3.75395650515625 4.291711595 35.571388925 0 0 1.923299045 0.36061857125 0.09012582125 0.36061857125 0.360618570833333 0 0 0.57698971375 0 0 4.4927063625 0 0.36061857125 6.7224414865 4.98366791375 14.693861174375 5.0486599925 3.812350655625 0.45335878 0.57698971375 0 0 0 0.28849485625 0.192329905 0.0901546425 0.75861343625 0.638004720416667 0 1.08012474375 0 0 0.701202776666667 6.41099681583333 4.46258006 1.844895903125 66.200256638125 2.02400088375 1.803092854375 7.40815500666667 0 1.95918148958333 0.244897686 0.26445361875 0 2.99746156125 17.65304155 10.29401009775 1.655850555625 0 4.55821873625 9.218733150625 2.02583861732143 2.80767315875 0.1502577375 0.022538660625 0.0500859125 1.62278356875 4.2501326034375 0.45077321375 0.45077321375 0.45077321375 1.097369109375 6.04603505675 0.48082476125 1.9769433 0.90236573375 6.499983475 1.682672411875 2.21953132953125 1.765584260625 0.0279732325 0.90875879875 6.588810391875 0.57698971375 0 7.26680508708333 0.9215807925 2.9213792628125 5.539926779375 0.432742285 7.1020329 39.5221038125 0.937608284375 2.548342743125 0.588154069375 0.9646546775 0 1.35231964125 0.450773213333333 0.0865484572916667 2.1233682525 0.72123714125 0 0 0.0180309284375 2.3957915178125 1.0714273771875 3.461938280625 0 1.98340214 0.150257738125 7.29898983625 1.16578395225 0 0.75128868875 0.409793830227273 3.40963932 0.0901546425 0.45077321375 4.327422850625 1.44247428375 2.163711425625 3.40165577375 0.86548457 0.432742285 0 2.59645371 0.436108058333333 3.3176908525 0 2.8849485671875 0 0 0 0 0 1.52902274 0.243417535625 0 0.979590745 3.534061995 2.8849485675 0.721237141875 0.436108058333333 0 0 0.348597951666667 2.4489768625 1.22448843125 0 0 0.813633725357143 0 0.04327422875 2.264959125625 0.60699317875 0.2163711425 0 0 0 1.5386392359375 0 0.0901546425 0 0.06491134325 0.358164360833333 1.2239995328125 0.96104849125 2.15270526125 0 11.646701763125 2.23583514 0.38946805625 2.15169080645833 0.9223821675 0.06851752875 0 0.342856760625 0.342856760625 0.12020619 0 0.0480824758333333 7.13454326958333 1.302167036875 1.2182266375 6.6985392375 2.565585270625 3.35013153875 0.3173443425 1.21648664625 1.26530471229167 2.64453618625 13.4693727434375 38.3457747083333 13.0612099323611 0 0.4370697075 0.0659727825 0.5904563 0 20.23164195625 6.69550274166667 1.4627590784375 0 3.7174365375 1.31504069625 0.76451137 0.61705844375 0 0.072123714375 0 19.5966891625 8.87332101333333 0.04038928 27.5411947695 1.4387739065625 0.375043313333333 16.7158997353125 4.219237279375 0.50486599875 0.50486599875 0.3966804275 0.37504331375 0.48082476125 0.48082476125 0.48082476125 0.48082476125 0.48082476125 0.12020619 1.68288666416667 45.4733495958333 0.72123714125 0.280481110833333 0 0 0 1.0818557125 0 0 0.432742285 0 0.04327422875 0 1.03281158708333 1.73096914 0.917755858333333 0 0.50486599875 4.98501917020833
feed_trans_CO2 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm const posnorm posnorm posnorm const posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm const posnorm const posnorm posnorm const posnorm posnorm const posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm const posnorm posnorm posnorm posnorm posnorm posnorm posnorm const const const posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm const posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm const posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm const const const posnorm posnorm posnorm posnorm const const const posnorm posnorm posnorm const posnorm posnorm posnorm posnorm posnorm posnorm const posnorm const posnorm posnorm posnorm posnorm const posnorm const posnorm posnorm posnorm posnorm const posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm const const posnorm posnorm posnorm posnorm posnorm posnorm posnorm const posnorm posnorm posnorm posnorm posnorm posnorm posnorm const const const const posnorm const posnorm posnorm posnorm posnorm posnorm posnorm posnorm const const posnorm posnorm posnorm posnorm posnorm const const posnorm const const posnorm const posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm const const const posnorm posnorm posnorm posnorm posnorm const posnorm const const posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm const posnorm posnorm posnorm const posnorm posnorm posnorm posnorm const posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm const posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm const posnorm posnorm posnorm posnorm posnorm const const posnorm posnorm posnorm posnorm const posnorm posnorm posnorm posnorm const posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm const posnorm posnorm posnorm const posnorm const const const const const posnorm posnorm const posnorm posnorm posnorm posnorm posnorm const const posnorm posnorm posnorm const const posnorm const posnorm posnorm posnorm posnorm const const const posnorm const posnorm const posnorm posnorm posnorm posnorm posnorm const posnorm posnorm posnorm posnorm posnorm posnorm const posnorm posnorm posnorm const posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm const posnorm posnorm posnorm const posnorm posnorm posnorm const posnorm posnorm posnorm posnorm const posnorm const posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm const const const posnorm const const posnorm const posnorm const posnorm posnorm posnorm const posnorm posnorm
live_weight_LW_1 lower 123.075328473 329.5218368205 336.779452251 322.56309925125 252.386422803 305.018580348 281.395150596 268.28315001 316.2405175065 286.2142020585 424.4095923255 326.678883417 576.2307128355 382.181714064 257.017164021 321.741110844 346.66728738075 325.1278006425 301.095893871 442.7967443805 270.80870548275 309.0185683155 236.038983885 576.2307128355 316.862523594 258.13785642525 272.1589767585 362.2601317275 448.12308619575 304.221157929 349.5397146435 329.95646149275 301.2499853055 240.3464761935 188.158036032
live_weight_LW_1 upper 164.820176727 441.2894773795 451.008740149 431.97046624875 337.991174397 408.475174852 376.839119804 359.27977399 423.5033830935 383.2927033415 568.3613838745 437.482247383 771.6773873645 511.810599536 344.192576379 430.869674756 464.25034391925 435.4050663575 403.221986529 592.9851138195 362.66195061725 413.8318838845 316.098990115 771.6773873645 424.336362006 345.69338667475 364.4702086415 485.1319892725 600.11805110425 407.407281671 468.0970447565 441.87151860725 403.4283428945 321.8675032065 251.977720768
live_weight_LW_1 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
live_weight_LW_2 lower 286.2142020585 267.250715622 399.932597349 207.265774905 249.111776394
live_weight_LW_2 upper 383.2927033415 357.897157178 535.582250251 277.566447095 333.605829206
live_weight_LW_2 distribution posnorm posnorm posnorm posnorm posnorm
live_weight_LW_3 lower 95.72028170175 72.5349754359 184.47760611 133.6473443925 196.8160710105 320.353210035 157.91220818325 204.330658743 382.181714064 238.9913487315 179.3540713635 168.295101309 203.349290256 236.6147266065 254.20482239625 173.7139031235 155.66989347675 262.6393079205 258.0791833755 223.4140941555 216.1606211145 206.000381289 224.7230292165 268.8802956045 240.3464761935 74.726241243075 205.859850867 170.0275092345 353.7333220635 123.075328473 197.1794892735 153.4122537075 130.260629115 237.456366975 223.4140941555 235.987469964 123.075328473 143.860203882 204.9033634785 259.1221039965 296.0101656855 207.265774905
live_weight_LW_3 upper 128.18680999825 97.1374817241 247.04895789 178.9780226075 263.5723991895 429.011023965 211.47307411675 273.635794457 511.810599536 320.0527418685 240.1876160365 225.377650291 272.321564144 316.8700139935 340.42634110375 232.6344082765 208.47020822325 351.7216462795 345.6148128245 299.1919740445 289.4782586855 275.871855644333 300.9448753835 360.0794601955 321.8675032065 100.071983886925 275.683659933 227.6976585655 473.7130453365 164.820176727 264.0590821265 205.4468192925 174.442596885 317.997123025 299.1919740445 316.030003636 164.820176727 192.654892918 274.4027499215 347.0114726035 396.4112745145 277.566447095
live_weight_LW_3 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
live_weight_LW_4 lower 348.2119015245 316.2405175065 316.2405175065 299.873979171 244.7039893365 335.2038117105 258.13354295025 313.1704551555 363.8818274985 469.513083708 240.3464761935 330.927186555 337.335352731 273.911092434 177.2557987635 311.102504361 331.9797314835 309.082263507 318.42867142575 443.3827020435 418.2029193825 316.2405175065 250.5048828705 335.510578044 407.8193019435 385.60004925075 306.898323669 424.4095923255 500.0729047065 339.09593987475 276.62838606 321.5478234735 323.459290629 311.102504361 300.9875770845 333.9563127885 267.250715622 311.046829497 296.0101656855 238.1778711225 306.4436320185 249.111776394 341.859508338 318.1019768235 349.246655385 376.379046405 195.6592406205 333.95955435 299.311293924 299.4196582485 337.335352731 314.04888891 271.9136579355 307.2476983005 303.042729555 277.527247218 276.62838606 286.2142020585 276.73245884025 316.2405175065 267.250715622 443.3827020435 199.4805311085 311.102504361 258.0791833755 231.7812587325 275.30467869375 359.3493956055 306.018273708 256.1089094055 329.253502338 244.7039893365 286.0101850707 316.2405175065 286.2142020585 399.932597349 316.133886609 299.6126258205 311.102504361 231.7812587325 258.0791833755 324.4576215465 281.45360681775
live_weight_LW_4 upper 466.3188622755 423.5033830935 423.5033830935 401.585621229 327.7030032635 448.898671822833 345.68761014975 419.3920130445 487.3037339015 628.763135492 321.8675032065 443.171495445 451.753191669 366.816609166 237.3776486365 416.622652039 444.5810439165 413.917183293 426.43371787425 593.7698173565 560.0495236175 423.5033830935 335.4714513295 449.309487556 546.1439774565 516.38837004925 410.992491931 568.3613838745 669.6882758935 454.11093702525 370.45555794 430.6108279265 433.170628971 416.622652039 403.0769307155 447.2280446115 357.897157178 416.548093303 396.4112745145 318.9633478775 410.3835773815 333.605829206 457.811856195333 425.9962145765 467.704585281667 504.039775595 262.0231935795 447.23238565 400.832083676 400.9772031515 451.753191669 420.56839509 364.1416822645 411.4603678995 405.829152445 371.659295982 370.45555794 383.2927033415 370.59493025975 423.5033830935 357.897157178 593.7698173565 267.1405942915 416.622652039 345.6148128245 310.3971242675 368.68287380625 481.233985927833 409.813945492 342.9762587945 440.930128862 327.7030032635 383.0194876093 423.5033830935 383.2927033415 535.582250251 423.360584991 401.235621712833 416.622652039 310.3971242675 345.6148128245 434.5075750535 376.91740328225
live_weight_LW_4 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
live_weight_LW_5 lower 305.4130121115 256.703503167 342.989301861 293.456091132 300.9875770845 288.2312211015 248.64210843525 220.02332823 433.62130123575 253.570090275 303.9240940785 347.80667265 295.6261886055 286.8792077205 223.4140941555 319.3416401265 271.9136579355 312.70950640125 316.336497156 207.265774905 262.6393079205 244.7039893365 231.523560108 289.1766099915 264.2791710735 326.8001356695 359.379622791 305.468787267 286.2142020585 262.6393079205 300.9875770845 399.932597349 337.335352731 299.6194449015 249.111776394 160.000309233 240.3464761935 326.678883417 203.349290256 348.6560008365 337.58294162175 286.2142020585 267.250715622 326.841244432875 312.0067324395 298.2592956825 281.395150596 306.78198027075 308.591598843 321.559302789 300.9875770845 286.2142020585 311.102504361 353.9842354935 334.443425127 332.007118074 276.73245884025 267.250715622 292.779115983 326.678883417 382.181714064 331.9797314835 353.7333220635 283.9619118105 262.74187410525 370.633118256 258.0791833755 315.3765706155 207.265774905 388.0413656775 393.958245519 418.2029193825 305.757547956 271.9136579355 267.250715622 342.745993827 293.4982001385 412.054691076 342.745993827 436.9992416055 436.9992416055 348.9058037925 331.491879171 340.765113411 321.43256118 306.018273708 297.75449825325 317.000958631875 303.26281596 260.84373161175 323.59395206025 277.1599488795 314.22636754425 321.3746351865 316.953600417 314.291217391875 211.2302417265 393.958245519 306.018273708 326.678883417 294.81515727975 361.05884775 405.964664757 306.018273708 346.30978195575 322.599620106 296.0101656855 353.1672608775 337.6639384935 364.943684916 331.186945473 382.181714064 289.27069868025 240.3464761935 348.32292699375 329.782814712 326.7380790855 285.886357923 324.2568083805 348.2119015245 321.43256118 316.2405175065 249.111776394 300.885241422 316.2405175065 253.570090275 280.5451048935 288.80798157225 262.20083562 294.65909566875 262.199437182 117.5137902225 307.5904424925 258.0791833755 316.2405175065 311.102504361 326.5623252 376.379046405 291.01832671725 300.9875770845 242.4342028815 359.3105012565 334.7123056695 326.678883417 221.130506205 288.9961998615 270.667026639 281.5848652635 292.544820504 311.102504361 211.2302417265 311.102504361 311.102504361 276.62838606 376.379046405 326.6112003345 335.476713375 364.943684916 325.93684794075 222.72762589875 418.2029193825 252.560519442 302.931748674 322.9505462205 267.250715622 323.920997490375 244.7039893365 359.3105012565 291.085790535 338.2316888745 337.335352731 337.335352731 240.91559523 311.102504361 303.90885716625 341.111904125625 321.940102689 215.242946793 259.024351077 321.2280512505 316.2405175065 295.3921120065 282.9595778685 269.202109995 290.9254853025 314.60269562325 239.10171080625 300.9856595475 235.368228177 331.9797314835 270.414411951375 353.7333220635 253.570090275 333.786364695 336.54956724675 281.2827405825 296.0101656855 331.328090754 454.478231727 314.962840305 240.292318014 294.1662107835 298.682355389625 231.7812587325 282.3209633205 393.958245519 370.633118256 524.6872550055 412.054691076 326.678883417 430.6749522975 312.980585904 267.250715622 311.102504361 315.6710493735 297.693140931 258.7170672405 281.395150596 207.265774905 267.217059402 275.6468709405 276.62838606 286.2142020585 315.823453038 256.3336849725 252.759173904 231.7812587325 296.0101656855 337.335352731 321.43256118 299.4882950835 306.2317456905 328.130153940375 302.763325986 219.3041457495 300.9875770845 217.75975749675 312.063603876 282.21908181975 247.416988554 327.360449574 291.085790535 342.745993827 306.018273708 283.55634987075 306.98888715 290.320766973 286.2142020585 191.8851615225 297.3242861505 258.0791833755 228.6639701145 262.6393079205 259.88162413725 282.521865834 353.7333220635 337.335352731 327.360449574 316.2405175065 281.860984692 248.2568392365 316.2405175065 294.24306284325 303.8346104625 330.4292014665 341.2262773545 405.964664757 276.62838606 349.12057496175 300.41258860125 342.745993827 300.9875770845 326.678883417 382.181714064 160.000309233 296.0101656855 316.2405175065 327.360449574
live_weight_LW_5 upper 409.0033904885 343.772527633 459.324854539 392.990905668 403.0769307155 385.9938574985 332.97685866475 294.65112377 580.69753206425 339.576319725 407.0094593215 465.77618735 395.8970595945 384.1832664795 299.1919740445 427.6563484735 364.1416822645 418.77471909875 423.631917244 277.566447095 351.7216462795 327.7030032635 310.052019092 387.2599046085 353.9177203265 437.6446261305 481.274465609 409.078083533 383.2927033415 351.7216462795 403.0769307155 535.582250251 451.753191669 401.2447536985 333.605829206 214.269419967 321.8675032065 437.482247383 272.321564144 466.9135917635 452.08475807825 383.2927033415 357.897157178 437.699678217125 417.8335773605 399.4232673175 376.839119804 410.83668702925 413.260094357 430.626200811 403.0769307155 383.2927033415 416.622652039 474.0490639065 447.880376339667 444.617719526 370.59493025975 357.897157178 392.084313217 437.482247383 511.810599536 444.5810439165 473.7130453365 380.2764783895 351.85900099475 496.344936144 345.6148128245 422.3464015845 277.566447095 519.6577353225 527.581510081 560.0495236175 409.464786444 364.1416822645 357.897157178 458.999020973 393.0472972615 551.815931324 458.999020973 585.2212065945 585.2212065945 467.2481232075 443.927721229 456.346262989 430.45647082 409.813945492 398.74725204675 424.521751618125 406.12388804 349.31704408825 433.35096503975 371.1674169205 420.80607115575 430.3788974135 424.458330383 420.892916858125 282.8755868735 527.581510081 409.813945492 437.482247383 394.81094162025 483.52325225 543.660282043 409.813945492 463.77157934425 432.019374294 396.4112745145 472.954986789167 452.193227573167 488.725753484 443.519359727 511.810599536 387.38590641975 321.8675032065 466.46754550625 441.638974088 437.5615211145 382.853660610333 434.2386498195 466.3188622755 430.45647082 423.5033830935 333.605829206 402.939884711333 423.5033830935 339.576319725 375.7007545065 386.76624432775 351.13445238 394.60194683125 351.132579618 157.3722687775 411.9193645075 345.6148128245 423.5033830935 416.622652039 437.3261548 504.039775595 389.72629718275 403.0769307155 324.6633477185 481.1818993435 448.2404561305 437.482247383 296.133835795 387.0183027385 362.472216961 377.0931821365 391.770549096 416.622652039 282.8755868735 416.622652039 416.622652039 370.45555794 504.039775595 437.3916074655 449.264136625 488.725753484 436.48852735925 298.27266860125 560.0495236175 338.224321358 405.680528926 432.4893279795 357.897157178 433.788938159625 327.7030032635 481.1818993435 389.816643465 452.953548258833 451.753191669 451.753191669 322.62965677 416.622652039 406.98905433375 456.810678624375 431.136160911 288.249326407 346.880563723 430.1825949495 423.5033830935 395.5835885935 378.9341715315 360.510428005 389.6019656975 421.31004267675 320.20053669375 403.074362785833 315.200726623 444.5810439165 362.133920098625 473.7130453365 339.576319725 447.000453305 450.70088245325 376.6885824175 396.4112745145 443.708378846 608.628743073 421.792341695 321.794975586 393.9418846165 399.989820960375 310.3971242675 378.0789508795 527.581510081 496.344936144 702.6513531945 551.815931324 437.482247383 576.7518367025 419.137743696 357.897157178 416.622652039 422.7407620265 398.665083469 346.4690549595 376.839119804 277.566447095 357.852085398 369.1411312595 370.45555794 383.2927033415 422.944858162 343.2772740275 338.490355696 310.3971242675 396.4112745145 451.753191669 430.45647082 401.0691203165 410.099823176167 439.425761709625 405.454980414 293.6880080505 403.0769307155 291.61979220325 417.909738524 377.94251308025 331.336201046 438.394988026 389.816643465 458.999020973 409.813945492 379.73335742925 411.11377285 388.792138227 383.2927033415 256.9690174775 398.1711200495 345.6148128245 306.2225096855 351.7216462795 348.02860776275 378.347995766 473.7130453365 451.753191669 438.394988026 423.5033830935 377.462956108 332.4609133635 423.5033830935 394.04480345675 406.8896245375 442.5046031335 456.963845112167 543.660282043 370.45555794 467.53574073825 402.30691689875 458.999020973 403.0769307155 437.482247383 511.810599536 214.269419967 396.4112745145 423.5033830935 438.394988026
live_weight_LW_5 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
live_weight_LW_6 lower 316.33148647125 281.395150596 342.5563586745 299.7496032615 461.042535495 333.1697466465 335.6137566225 287.266716378 312.014376396 271.9136579355 325.0489490085 334.80640897875 360.2907549945 291.2773756005 424.4095923255 249.111776394 302.26068093375 348.15921484275 327.71452965525 295.37074872 343.66326198075 359.3105012565 344.0799727614 355.9914692325 363.20835105225 281.0308054275 319.45284574425 412.054691076 370.633118256 353.7333220635 296.0101656855 267.250715622 253.570090275 374.616508536 373.69245270825 388.0413656775 306.018273708 244.09969521525 364.943684916 353.5018072155 372.14429087025 382.181714064 326.678883417 337.335352731 305.1767063565 456.8241646815 331.9797314835 302.1296192235 418.2029193825 382.181714064 405.964664757 297.827951004 332.649125775 331.9797314835 348.2119015245 352.918327428 296.0101656855 249.111776394 286.2142020585 400.65403527675 300.9875770845 376.379046405 342.745993827 296.0101656855 316.2405175065 373.6372779765 298.7239917735 342.3780152505 286.2142020585 331.4745265185 306.018273708 289.718089956 331.3536695325 383.152020048 324.0124551945 370.633118256 311.102504361 375.104047662 482.9395704795 316.2405175065 337.335352731 462.8905251705 316.2405175065 332.0428101345 296.0101656855 399.932597349 341.5427703675 348.2119015245 291.085790535 337.60877989275 341.24325211125 318.070864827 293.3923699485 354.5646117165 211.2302417265 262.6393079205 309.976297677 280.8245052135 258.0791833755 331.9797314835 281.395150596 339.660252144 299.98598229 340.8071346945 327.132012726 320.2331087835 266.882596398 318.7950039135 279.6121312875 326.246880987 449.82557532 286.2142020585 337.756314546 288.533711808 231.7812587325 296.0101656855 244.7039893365 331.9797314835 296.9982024885 296.0101656855 348.938346204 353.7333220635 296.0201922705 308.019944304 307.32982588125 199.4805311085 326.678883417 321.43256118 292.8110118435 296.0101656855 371.058857298 361.533093165 333.3768296985 265.8879067545 337.335352731 334.409877207 311.102504361 308.019944304
live_weight_LW_6 upper 423.62520702875 376.839119804 458.7450651255 401.4190593385 617.419535838333 446.1746899535 449.4476623775 384.702210822 417.843814004 364.1416822645 435.299469724833 448.36647752125 482.4946368055 390.0732105995 568.3613838745 333.605829206 404.78184756625 466.24830525725 438.86916544475 395.55497928 460.22740931925 481.1818993435 460.7854605986 476.7371137675 486.40182684775 376.3511955725 427.80527295575 551.815931324 496.344936144 473.7130453365 396.4112745145 357.897157178 339.576319725 501.679417864 500.44193959175 519.6577353225 409.813945492 326.89374388475 488.725753484 473.4030049845 498.36867022975 511.810599536 437.482247383 451.753191669 408.6869342435 611.770372585167 444.5810439165 404.6063321765 560.0495236175 511.810599536 543.660282043 398.845618596 445.477484225 444.5810439165 466.3188622755 472.621619772 396.4112745145 333.605829206 383.2927033415 536.54838642325 403.0769307155 504.039775595 458.999020973 396.4112745145 423.5033830935 500.3680506235 400.0455796265 458.5062309495 383.2927033415 443.9044828815 409.813945492 387.985044444 443.7426334675 513.110015152 433.9114166055 496.344936144 416.622652039 502.332321138 646.7436353205 423.5033830935 451.753191669 619.8943290295 423.5033830935 444.6655176655 396.4112745145 535.582250251 457.3876866325 466.3188622755 389.816643465 452.11936020725 456.98657738875 425.954549973 392.9055714515 474.8262928835 282.8755868735 351.7216462795 415.114457123 376.0749221865 345.6148128245 444.5810439165 376.839119804 454.866653456 401.73561371 456.4025371055 438.089069674 428.8501866165 357.404178802 426.9243034865 374.4513337125 436.903717813 602.39799268 383.2927033415 452.316935854 386.398947392 310.3971242675 396.4112745145 327.7030032635 444.5810439165 397.7344349115 396.4112745145 467.291703396 473.7130453365 396.4247019295 412.494545296 411.57035161875 267.1405942915 437.482247383 430.45647082 392.1270275565 396.4112745145 496.915077902 484.158352835 446.4520117015 356.0721090455 451.753191669 447.835449593 416.622652039 412.494545296
live_weight_LW_6 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
live_weight_LW_7 lower 101.426451624 87.505527501 117.8022814155 87.205642975575 102.050655489 167.7584585925 133.1358755835 107.342960139 120.273970392 249.111776394 127.240566309 123.096050937 125.918131482 89.9164362139875 112.115891619 92.1176113365 50.89007545695 83.9549432655 286.2142020585 85.99390992765 79.6294582857 92.1176113365 103.204660446 79.2928647072 129.5473517775 96.3784935765 101.8024129935 55.84557835305 82.6442755812 106.9688862675 117.920504232 107.2727652375 47.74856658705 154.1648271015 100.801147716 123.1998499035 102.303317223 122.9527745685 75.83787944955 36.4299152229 59.3147335599 120.273970392 43.27380430155 64.7723654559 120.273970392 72.5349754359 101.8024129935 137.7091232955 63.02307804345 61.09980851925 296.0101656855 76.2807431583 80.86914194625 114.7945200075 24.9128484291 151.6372449795 153.4122537075 47.74856658705 80.044469287875 57.5634178737 119.3395618395 68.05081033065 107.282317554 106.07657364 96.3614248695 140.78466358875 103.291818462 66.6604103823 144.05506517925 89.7926038515 128.8026464355 199.4805311085 117.5137902225 120.273970392 30.32192773935 130.3080040665 70.54143579585 144.181385487 153.699726681 106.8794845605 240.3464761935 85.25610816345 207.265774905 156.6841042665 121.6424150775 29.18539251 237.1691658915 107.2538628975 112.115891619 149.2441644465 112.610785626 316.2405175065 163.3611304935 120.273970392 120.273970392 53.57434515255 66.6604103823 78.73104421575 117.5137902225 50.89007545695
live_weight_LW_7 upper 135.828405976 117.185764899 157.7586107845 116.784165154425 136.664328111 224.6589884075 178.293073149833 143.751683461 161.068650408 333.605829206 170.398185291 164.847927863 168.627205318 120.414408731013 150.143503981 123.3621812635 68.15103672305 112.4308889345 383.2927033415 115.16143493235 106.6382803943 123.3621812635 138.209749954 106.1875205728 173.4873892225 129.0682750235 136.3318864065 74.78735346695 110.6756672988 143.2507307325 157.916932568 143.6576797625 63.94398683295 206.4546514985 134.991010684 164.9869334965 137.002687977 164.6560548315 101.56066897045 48.7862607371 79.4331812001 161.068650408 57.95146891845 86.7419397041 161.068650408 97.1374817241 136.3318864065 184.4174809045 84.39932673655 81.82372018075 396.4112745145 102.1537437617 108.29844155375 153.7306729925 33.3628204109 203.0697608205 205.4468192925 63.94398683295 107.194055362125 77.0878520063 159.8173079605 91.13237172935 143.670472046 142.05576236 129.0454169305 188.53618691125 138.326470338 89.2703741377 192.91584752075 120.2485747485 172.4900937645 267.1405942915 157.3722687775 161.068650408 40.60655820065 174.5060405335 94.46777074415 193.085013313 205.831797719 143.1310056395 321.8675032065 114.17338461655 277.566447095 209.8284203335 162.9012459225 39.08453149 317.6125087085 143.6323661025 150.143503981 199.8649921535 150.806256774 423.5033830935 218.7701689065 161.068650408 161.068650408 71.74576046745 89.2703741377 105.43514108425 157.3722687775 68.15103672305
live_weight_LW_7 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
live_weight_LW_8 lower 99.322952067 92.559108798 94.55850108405 97.91384011875 97.0703387145 91.185227424 116.797015593 83.19832037775 95.9037262695 177.2557987635 128.8026464355 61.09980851925 92.1176113365 70.54143579585 89.0023471875 128.8026464355 117.5137902225 120.273970392 72.5349754359 96.8825128275 113.9090606325 59.3147335599 106.8794845605 87.942438486 54.16093120635 99.689534514 87.2416499625 91.176442641 39.07710868995 85.25610816345 68.5833405399 59.3147335599 99.2107255365 68.3834891805 90.2069626725 223.11860970375 140.7631441365 74.56423812165 117.5137902225 109.737243999 76.1014657665 75.33975867255 68.5833405399 91.16248545 85.5957214395 207.265774905
live_weight_LW_8 upper 133.011438733 123.953426402 126.63097513595 131.12438238125 129.9947810855 122.113550176 156.412377607 111.41763372225 128.4324755305 237.3776486365 172.4900937645 81.82372018075 123.3621812635 94.46777074415 119.1902778125 172.4900937645 157.3722687775 161.068650408 97.1374817241 129.7432481725 152.5448823675 79.4331812001 143.1310056395 117.770867914 72.53130553365 133.502359086 116.8323850375 122.101785759 52.33133269005 114.17338461655 91.8455262201 79.4331812001 132.8611470635 91.5778890195 120.8034763275 298.79626679625 188.5073684635 99.85503233835 157.3722687775 146.958063601 101.9136588335 100.89359494745 91.8455262201 122.08309455 114.6281883605 277.566447095
live_weight_LW_8 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
live_weight_LW_9 lower 236.038983885 156.6841042665 94.480823385 199.4805311085 134.697876651 213.9797512395 207.265774905 150.98190462225 181.2414647115 178.1387988165 203.349290256 321.43256118 262.6393079205 201.2895349785 267.250715622 169.8719782365 184.47760611 185.593071942 115.5516376923 173.7139031235 258.0791833755 173.465066574 104.3211668985 76.6295018991 163.3611304935 147.000566385 156.6839956815 220.159329831 230.623730064 316.2405175065 267.250715622 147.000566385 180.258348006 174.4663828665 131.7291395805 276.62838606 214.4560315545 103.8964196565 249.08815184625 334.0074893094 300.9875770845 314.2742522382 253.570090275 177.2557987635 231.7812587325 300.9875770845 203.349290256 163.3611304935 306.018273708 195.6592406205 225.2208457845 224.45701092045 306.042342813 155.8070286795 172.234079982 286.2142020585 299.332605141 225.0959394045 96.8825128275 78.73104421575 235.7700209775 243.91873301175 405.964664757 207.265774905 238.14910058625 331.9797314835 253.570090275 106.8794845605 213.8598304785 180.8436133755 203.349290256 399.932597349 223.4140941555 209.810680353 191.8851615225 305.2099101105 211.2302417265 262.6393079205 207.9542666475 267.250715622 167.6708289585 231.7812587325 131.9600087325 240.788012472 236.038983885 207.265774905 196.1832615705 210.219942326625 105.5946767445 248.1074055855 180.745008705 311.102504361 227.573046972 170.217666621 72.5349754359 87.505527501 117.5137902225 321.43256118 337.335352731 462.8905251705 296.0101656855 39.07710868995 166.7668292505 184.47760611 198.6854834235 156.6841042665 337.335352731 316.2405175065 207.2943087345 188.158036032 140.7631441365 311.102504361 231.7812587325 140.437460358 114.7945200075 96.8825128275 207.265774905 190.7828272935 94.480823385 131.7291395805 286.2142020585 153.4122537075 332.66438748225 267.250715622 293.7442949595 443.3827020435 215.242946793 180.1467712755 223.4140941555 296.0101656855 342.745993827 264.331386066 140.3577739305 311.102504361 206.6349279465 195.6592406205 286.2142020585 219.3041457495 240.3464761935 140.7631441365 227.573046972 131.7291395805 215.242946793 231.7812587325 125.918131482 203.9963098815 156.6841042665 96.8825128275 123.075328473 224.541911745 248.733164781 163.3611304935 276.62838606 262.66494949875 286.2142020585 153.4122537075 286.2142020585 177.2557987635 106.8794845605 104.3211668985 140.7631441365 311.102504361 156.6841042665
live_weight_LW_9 upper 316.098990115 209.8284203335 126.526950615 267.1405942915 180.384875749 286.5576785605 277.566447095 202.19214127775 242.7151778885 238.5601457835 272.321564144 430.45647082 351.7216462795 269.5631784215 357.897157178 227.4893743635 247.04895789 248.542768858 154.7445908277 232.6344082765 345.6148128245 232.301171026 139.7049545015 102.6207949409 218.7701689065 196.860407615 209.8282749185 294.833254569 308.846983536 423.5033830935 357.897157178 196.860407615 241.398606394 233.642115066833 176.4091986195 370.45555794 287.1955042455 139.1361409435 333.57419165375 447.2965792506 403.0769307155 420.8701974418 339.576319725 237.3776486365 310.3971242675 403.0769307155 272.321564144 218.7701689065 409.813945492 262.0231935795 301.6115420155 300.58862865955 409.846178387 208.6538571205 230.652656818 383.2927033415 400.860623259 301.444269728833 129.7432481725 105.43514108425 315.7388000225 326.65140268825 543.660282043 277.566447095 318.92481891375 444.5810439165 339.576319725 143.1310056395 286.3970829215 242.1823828245 272.321564144 535.582250251 299.1919740445 280.974536847 256.9690174775 408.7314000895 282.8755868735 351.7216462795 278.4884623525 357.897157178 224.5416364415 310.3971242675 176.7183742675 322.458800328 316.098990115 277.566447095 262.7249526295 281.522612823375 141.4104150555 332.2607946145 242.050333295 416.622652039 304.761565828 227.952313779 97.1374817241 117.185764899 157.3722687775 430.45647082 451.753191669 619.8943290295 396.4112745145 52.33133269005 223.3310169495 247.04895789 266.075881309833 209.8284203335 451.753191669 423.5033830935 277.6046590655 251.977720768 188.5073684635 416.622652039 310.3971242675 188.071218842 153.7306729925 129.7432481725 277.566447095 255.4927921065 126.526950615 176.4091986195 383.2927033415 205.4468192925 445.49792241775 357.897157178 393.3768628405 593.7698173565 288.249326407 241.2491849245 299.1919740445 396.4112745145 458.999020973 353.987645667333 187.9645042695 416.622652039 276.7216286535 262.0231935795 383.2927033415 293.6880080505 321.8675032065 188.5073684635 304.761565828 176.4091986195 288.249326407 310.3971242675 168.627205318 273.1880407185 209.8284203335 129.7432481725 164.820176727 300.702326255 333.098799619 218.7701689065 370.45555794 351.75598500125 383.2927033415 205.4468192925 383.2927033415 237.3776486365 143.1310056395 139.7049545015 188.5073684635 416.622652039 209.8284203335
live_weight_LW_9 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
live_weight_LW_10 lower 74.56423812165 51.5385406395 49.43075420655 39.07710868995 31.4864210754 20.16512779545 40.4455946976 37.7386464006 41.8443962265 54.74302363665 36.4857499035 54.16093120635 40.0478007207 19.7374062906 26.5777536771 34.4414136447 47.74856658705 34.62314713515 54.16093120635 78.73104421575 92.1176113365 77.156888586525 37.2522416373 40.4455946976 48.84437200185 32.93993811555 74.56423812165 48.18333843045 22.4156283489 31.9201548633 42.53298026625 47.4480539316 49.3032958596 30.32192773935 31.4864210754 32.67917016075 73.535759327025 29.18539251 85.25610816345 33.3962237511 65.372708853 46.2256003593 36.4299152229 96.8825128275 50.89007545695 29.18539251 50.89007545695 72.5349754359 39.4764925194 68.5833405399 30.32192773935 61.09980851925
live_weight_LW_10 upper 99.85503233835 69.0194491605 66.19674101345 52.33133269005 42.1660258846 27.00476178455 54.1639835424 50.5388890394 56.0372323735 73.31083282335 48.8610334965 72.53130553365 53.6312652926333 26.4319651494 35.5924303629 46.1232966353 63.94398683295 46.36667072485 72.53130553365 105.43514108425 123.3621812635 103.327061323475 49.8875048827 54.1639835424 65.41146893815 44.11254870445 99.85503233835 64.52622514955 30.0185900111 42.7468740567 56.95937123375 63.5415459084 66.0260511804 40.60655820065 42.1660258846 43.76333313925 98.477712782975 39.08453149 114.17338461655 44.7235978889 87.545908347 61.9044589607 48.7862607371 129.7432481725 68.15103672305 39.08453149 68.15103672305 97.1374817241 52.8661800406 91.8455262201 40.60655820065 81.82372018075
live_weight_LW_10 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
live_weight_LW_11 lower 47.74856658705 51.295972494 53.77074424425 49.3032958596 54.59001704325 68.5833405399 87.505527501 49.36366531485 89.7926038515 65.69714716155 68.3272023417 51.3773564157 55.84557835305 37.7386464006 39.602587338 61.30237594005 10.64047384575 59.63918091435 61.09980851925 37.7386464006 61.23192342705 46.2256003593 83.0440713582 65.742531685875 46.2256003593 68.5833405399
live_weight_LW_11 upper 63.94398683295 68.6946064393333 72.00877445575 66.0260511804 73.10592925675 91.8455262201 117.185764899 66.10689682515 120.2485747485 87.98039005845 91.5025107383 68.8035942643 74.78735346695 50.5388890394 53.035043862 82.09499467995 14.24952345425 79.86767502565 81.82372018075 50.5388890394 82.00064599295 61.9044589607 111.2110663218 88.041168164125 61.9044589607 91.8455262201
live_weight_LW_11 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
live_weight_LW_12 lower 184.47760611 248.36121567 236.038983885 258.0791833755 267.250715622 258.5911406175 276.0693931305 267.250715622 262.6393079205 270.8262949275 245.31592065975 281.2965327585 268.625928573 273.2336381715 290.27960876925 370.633118256 376.379046405 267.250715622 242.3783537685 303.9203773935 354.977938467 359.3105012565 418.2029193825 272.978754264 203.349290256 247.5477220725 382.181714064 267.887345544 351.0257692815 219.3041457495 231.7812587325 291.085790535 331.9797314835 261.620192979 177.2557987635 262.163719899 311.102504361 321.43256118 300.9875770845 342.745993827 263.907458028 203.349290256 248.8077432675 131.7291395805 262.8060937965 215.98558017075 353.7333220635 250.73826328575
live_weight_LW_12 upper 247.04895789 332.60069233 316.098990115 345.6148128245 357.897157178 346.3004163825 369.7069650695 357.897157178 351.7216462795 362.6855060725 328.52249024025 376.7070526415 359.738816627 365.909375095167 388.73701993075 496.344936144 504.039775595 357.897157178 324.5885556315 407.0044820065 475.379812333 481.1818993435 560.0495236175 365.568039336 272.321564144 331.5112769275 511.810599536 358.749720056 470.0871413185 293.6880080505 310.3971242675 389.816643465 444.5810439165 350.356866621 237.3776486365 351.084747701 416.622652039 430.45647082 403.0769307155 458.999020973 353.419929172 272.321564144 333.1986737325 176.4091986195 351.9450028035 289.24384712925 473.7130453365 335.78399001425
live_weight_LW_12 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
weight_gain_WG_1 lower 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
weight_gain_WG_1 upper 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
weight_gain_WG_1 distribution const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const
weight_gain_WG_2 lower 0 0 0 0 0
weight_gain_WG_2 upper 0 0 0 0 0
weight_gain_WG_2 distribution const const const const const
weight_gain_WG_3 lower 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225 0.124567083225
weight_gain_WG_3 upper 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775 0.166817906775
weight_gain_WG_3 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
weight_gain_WG_4 lower 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
weight_gain_WG_4 upper 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
weight_gain_WG_4 distribution const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const
weight_gain_WG_5 lower 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
weight_gain_WG_5 upper 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
weight_gain_WG_5 distribution const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const
weight_gain_WG_6 lower 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
weight_gain_WG_6 upper 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
weight_gain_WG_6 distribution const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const const
weight_gain_WG_7 lower 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485
weight_gain_WG_7 upper 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515
weight_gain_WG_7 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
weight_gain_WG_8 lower 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875
weight_gain_WG_8 upper 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125
weight_gain_WG_8 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
weight_gain_WG_9 lower 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588
weight_gain_WG_9 upper 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612
weight_gain_WG_9 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
weight_gain_WG_10 lower 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485 0.358848550485
weight_gain_WG_10 upper 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515 0.480563263515
weight_gain_WG_10 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
weight_gain_WG_11 lower 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875 0.292552720875
weight_gain_WG_11 upper 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125 0.391781129125
weight_gain_WG_11 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
weight_gain_WG_12 lower 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588 0.2223483588
weight_gain_WG_12 upper 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612 0.2977647612
weight_gain_WG_12 distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
DE_perc lower 48.533208165 54.204245538 55.689237435 51.350656797 58.841055864 50.669213373 48.341254719 50.732889765 54.221436039 53.7804267615 57.6385882308 50.39929155 47.6042422725 53.7779920564286 52.3029974985 49.580976036 48.9704192892 51.8410825308 50.01738809 54.789334218 55.5932294656364 46.279158651 46.507673745 43.478975547 59.5800523125 49.20049026 54.0993495435 54.925065816 54.45358463925 51.7090802085 51.23281652775 56.774835066 56.3452654635 51.652279752 56.927428794 59.656718772 54.481720068 47.069339112 54.4052180565 54.2626189335 57.668877126 55.795094796 54.04157226 49.6942857675 52.2756089235 51.967934334 50.74522689 51.3141088185 53.9916753555 50.9551814025 50.490809334 48.98087535 64.38571092225 49.7410944340909 54.34301153025 52.877676258 44.21417167575 47.48323614975 48.3828462684 60.51532689 55.5067063755 51.4315041408 51.691610232 50.50407826125 55.369592685 53.44067763 55.519124454 55.2755965995 46.718433834 49.336687989 55.922831757 54.772960122 51.4622377035 51.615693402 56.56117314375 53.451969291 54.501939936 54.6725697768 47.3082407235 52.6085724015 53.60473368675 53.606565486 53.9341974585 52.8466416825 49.9687647165 52.5026736225 54.292494402 50.910037866 56.2495380795 55.3364769465 50.27879835 51.045844557 51.553028619 56.5984436175 52.5857704326 50.261200344 51.53443029 53.6021838 54.0682313535 56.499041463 52.265319853 55.8266792382 53.49274418025 48.889109565 54.5766156315 52.7566128345 52.5288478842 49.732932174 49.47546798 51.375740277 54.779637876 49.34597420025 54.881576277 54.92730644775 51.846217746 55.2255949144286 58.582362717 66.106895544 51.938376372 51.516387594 51.021751068 49.016408226 49.5612066045 52.132908429 49.326804948 50.9855542884 49.066805025 52.221296772 51.7318016661 50.625568584 55.97810245575 59.6178205866 49.723887915 50.132369196 52.64365638825 50.3991614835 53.8999528932 57.76735879125 56.3862870972 60.5431921095 59.8474203165 54.4690908315 52.679291275125 54.1267569645 49.77 52.583084022 47.3646044574 49.975592634 56.7111030975 54.643937153625 59.8707111672 57.0746790045 50.701337202 59.196677517 50.153958195 49.288598073 52.308116253 54.03532225275 61.559241444 53.4190238505 57.521104974 56.6775 47.845488276 56.7557182125 58.442580756 50.2560093546 51.2172802485 58.7140540515 51.9342670395 55.560338955 49.02808347 54.977142861 55.701157044 50.04 51.987366297 53.723377557 51.1406368155 55.52195935275 54.713629881 50.6800999005 52.8608536515 60.0558068754 55.2782940525 49.712466183 50.813486568 53.201390661 59.2100594145 52.704355968 50.787868077 56.746252872 53.6206086945 55.54848479175 55.49298209775 56.110773825 52.02506524275 54.645639876 54.6677793036 49.583180025 48.937515489 46.963329723 52.9594018965 51.952202865 52.11145968 50.0386918815 52.291719876 47.1679802595 54.225847623 54.3922161 51.767203443 57.014760342 59.739304509 49.406598549 52.680113766 53.427908226 58.4368351065 55.499505804 59.6330712918 50.67213687 55.047771207 54.453270942 57.2221843245 58.2920511894 53.1909231345 53.7168402855 52.275836952 53.148793905 49.7261125414286 53.612894331 50.983222971 54.1454194305 50.898371958 55.502952201 50.08119275925 51.8901109245 49.78636032375 50.7678344145 53.5666181985 52.2915886134 48.959109333 51.194064771 57.204274497 50.15596916475 51.49586589975 53.02147391775 51.65051733225 54.3425847525 54.158201343 55.653615783 49.4498500905 51.040938861 54.738580245 51.7038626835 50.3444762055 50.493472533 51.694890687 54.779031297 48.46621343175 50.86232037 50.409675396 58.639068486 47.283473832 49.094513541 52.851553758 50.491157268 51.3003125415 53.348156262 49.069616985 50.599445202 50.192983314 55.525306257 51.035838649875 51.258863106 52.4755237635 52.89432663 55.5728009985 50.294066454 50.649984306 53.6106832542 51.5769880635 53.993471895 54.2180810544546 53.716329054 49.8133263285 54.3064013145 57.2904010935 60.748756011 54.809524638 52.720948638 48.1842727542 51.6229462035 52.684359558 50.062576518 49.114297194 55.1889117684 50.7120444792 53.5829704785 55.714040997 62.370326448 53.534427459 55.401278058 53.015024808 56.987824914 54.445564071 54.9314711865 48.8148393645 51.4113725835 56.243285118 53.8927608375 48.665823039 57.463183926 41.6525398362 50.072058324 47.91918267 56.597194278 52.3629724095 55.199355369 50.0508810334286 52.556665821 49.49289117 51.99304995 49.5355414095 47.869604928 49.802821731 47.774789052 48.447417825 52.03384174125 50.4158970285 50.380158726 49.162698072 47.8742197824 52.990918962 56.10770467875 57.272028219 53.116494336 51.0759401175 55.69981821 60.684512328 54.481886625 60.1321125315 55.1704800555 56.589597522 50.22 52.9501781385 52.94424879 53.1551862495 54.141088071 54.959339091 48.844393278 53.8694256375 49.938215274 50.791763484 51.056273502 51.715953321 49.4788145904 50.721780321 53.15164325025 55.234885986 54.86625688275 53.154617073 54.3980695 49.50288715275 50.312614149 49.685637042 50.984020884 59.411728935 53.938705878 54.127347393 52.14107549025 52.277198322 52.06241817 50.3458194105 53.420640858 49.711318869 50.069561409 49.8436726365 55.587607893 50.328816867 49.0760365815 48.455372214 57.3249982023 54.434133468 47.169720399 51.2869960665 58.473493371 50.372293239 52.201721322 55.7650483065 49.3263914625 48.426043437 54.208955187 54.212794164 54.211049595 54.20675259 52.896596805 54.825650373 64.64412447 55.1387597985 54.216725811 55.040530968 55.177847388 55.026 51.359398848 52.938 53.3660655195 47.52541595025 54.389920254 49.48399494 55.241289612 50.213155275 60.064998777 52.477884279 54.73894716 50.336833005 55.36074306975
DE_perc upper 59.318365535 66.2496334353333 68.0646235316667 62.761913863 71.916846056 61.929038567 59.0837557676667 62.0068652683333 66.2706440476667 65.7316327085 70.4471633932 61.5991341166667 58.1829627775 65.7286569578572 63.9258858315 60.5989707106667 59.8527346868 63.3613230932 61.1323632211111 66.964741822 67.947280458 56.563416129 56.842712355 53.140970113 72.8200639375 60.13393254 66.1214272198333 67.1306359973333 66.55438122575 63.1999869215 62.61788686725 69.3914650806667 68.8664355665 63.1305641413333 69.577968526 72.913767388 66.588768972 57.529192248 66.4952665135 66.3209786965 70.484183154 68.1940047506667 66.05081054 60.7374603825 63.8924109065 63.516364186 62.0219439766667 62.7172441115 65.9898254345 62.2785550475 61.710989186 59.8655143166667 78.69364668275 60.794670975 66.41923631475 64.628270982 54.03954315925 58.03506640525 59.1345898836 73.96317731 67.8415300145 62.8607272832 63.178634728 61.72720676375 67.673946615 65.31638377 67.856707666 67.5590625105 57.1003080193333 60.300396431 68.350127703 66.944729038 62.8982905265 63.0858474913333 69.13032273125 65.330184689 66.613482144 66.8220297272 57.8211831065 64.2993662685 65.51689672825 65.519135594 65.9195746715 64.5903398341667 61.0729346535 64.1699344275 66.357493158 62.223379614 68.7494354305 67.6334718235 61.45186465 62.3893655696667 63.009257201 69.1758755325 64.2714971954 61.430355976 62.98652591 65.5137802 66.0833938765 69.0543840103333 63.8798353758889 68.2326079578 65.38002066475 59.753356135 66.7047524385 64.4803045755 64.2019251918 60.7846948793333 60.47001642 62.7925714496667 66.9528907373333 60.31174624475 67.0774821163333 67.13337454725 63.3675994673333 67.4979493398571 71.600665543 80.797316776 63.480237788 62.964473726 62.359917972 59.9089433873333 60.5748080721667 63.717999191 60.2883171586667 62.3156774636 59.970539475 63.826029388 63.2277575919 61.875694936 68.41768077925 72.8662251614 60.773640785 61.272895684 64.34224669675 61.5989751465 65.8777202028 70.60454963375 68.9165731188 73.9972348005 73.1468470535 66.5733332385 64.385800447375 66.1549251788333 60.83 64.2682138046667 57.8900721146 61.081279886 69.3135704525 66.787034298875 73.1753136488 69.7579410055 61.9683010246667 72.351494743 61.2992822383333 60.241619867 63.932142087 66.04317164225 75.239072876 65.2899180395 70.303572746 69.2725 58.477819004 69.3681000375 71.429820924 61.4240114334 62.5988980815 71.7616216185 63.4752152705 67.907080945 59.92321313 67.194285719 68.0791919426667 61.16 63.540114363 65.661905903 62.5052227745 67.86017254225 66.872214299 61.9423443228333 64.6077100185 73.4015417366 67.5623593975 60.7596808903333 62.105372472 65.023921919 72.3678503955 64.416435072 62.074060983 69.356531288 65.5362995155 67.89259252325 67.82475589725 68.579834675 63.58619085225 66.789115404 66.8161747044 60.601664475 59.812518931 57.399625217 64.7281578735 63.497136835 63.6917840533333 61.1584011885 63.9121020706667 57.6497536505 66.2760359836667 66.4793752333333 63.2710264303333 69.6847070846667 73.014705511 60.385842671 64.386805714 65.3007767206667 71.4227984635 67.832729316 72.8848649122 61.93261173 67.280609253 66.553997818 69.9382252855 71.2458403426 65.0111282755 65.6539159045 63.892689608 64.959636995 60.7763597728571 65.526870849 62.3128280756667 66.1777348595 62.209121282 67.836941579 61.21034670575 63.4212466855 60.84999595125 62.0495753955 65.4703111315 63.9119416386 59.838911407 62.570523609 69.9163354963333 61.30174009025 62.93939165525 64.80402367725 63.12841007275 66.4187146975 66.193357197 68.021085957 60.4387056661667 62.383369719 66.9027091883333 63.1936099465 61.5321375845 61.714244207 63.182644173 66.952149363 59.23648308325 62.16505823 61.611825484 71.669972594 57.7909124613333 60.004405439 64.596343482 61.7114144386667 62.7003819951667 65.203302098 59.973976315 61.843766358 61.346979606 67.864263203 62.377136127625 62.649721574 64.1367512665 64.6486214366667 67.9223123315 61.470525666 61.905536374 65.5241684218 63.0385409665 65.992021205 66.266543511 65.653291066 60.8829544015 66.3744904955 70.0216013365 74.248479569 66.989419002 64.436715002 58.8918889218 63.0947120265 64.3919950153333 61.187593522 60.0285854593333 67.4531143836 61.9813876968 65.4902972515 68.0949389963333 76.230398992 65.4309668943333 67.712673182 64.796141432 69.651786006 66.544578309 67.1384647835 59.6625814455 62.8361220465 68.741792922 65.8689299125 59.480450381 70.232780354 50.9086597998 61.199182396 58.56788993 69.174348562 63.9991885005 67.4658787843333 61.1732990408571 64.2359248923333 60.49131143 63.54706105 60.5434395005 58.507294912 60.870115449 58.3914088413333 59.213510675 63.59691768375 61.6194297015 61.575749554 60.087742088 58.5129352896 64.7666787313333 68.57608349625 69.999145601 64.920159744 62.4261490325 68.07755559 74.169959512 66.5889725416667 73.4948042051667 67.4305867345 69.165063638 61.38 64.7168843915 64.70963741 64.9674498605 66.1724409756667 67.1725255556667 59.6987028953333 65.8404091125 61.035596446 62.078822036 62.402112058 63.2083873923333 60.4741067216 61.993287059 64.9631195280833 67.509305094 67.05875841225 64.9667542003333 66.4865293888889 60.50352874225 61.493195071 60.726889718 62.3138033026667 72.614335365 65.925084962 66.1556468136667 63.72798115475 63.8943535046667 63.63184443 61.5337792795 65.291894382 60.7582786176667 61.196130611 60.9200443335 67.940409647 61.512998393 59.9818224885 59.223232706 70.0638866917 66.530607572 57.6518804876667 62.6841063035 71.467603009 61.566136181 63.802103838 68.1572812635 60.2878117875 59.187386423 66.255389673 66.260081756 66.257949505 66.25269761 64.651396095 67.0091282336667 79.0094854633333 67.3918175315 66.2648871023333 67.271760072 67.439591252 67.254 62.772598592 64.702 65.2251911905 58.08661949475 66.4765691993333 60.48043826 67.517131748 61.371634225 73.412776283 64.139636341 66.90315764 61.522795895 67.6631304185833
DE_perc distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm
perc_crude_protein_diet lower 9.037608459 9.97640889 10.346797038 9.2438503938 9.24515243678571 8.5511855121 6.7469072223 8.4402879759 8.8841910504 8.4423663342 9.638112411 9.612512085 7.3315333845 10.3808438215714 12.4497285825 8.8181124624 8.98430521824 9.5463880542 8.1589667898 9.9514086354 10.1126637271636 5.2240818195 7.3535580801 5.69247030405 8.4047886999 9.524863767 7.9088909259 7.5334165755 9.715897335825 9.2002302 9.99322990425 9.0007671117 6.39477306315 8.4842653221 9.067262010075 5.12951285565 10.352278179 4.4161242588 10.3598032095 7.50003736185 9.093489516 9.922407006 9.913690116 7.76740601565 8.75667633885 12.358870362 8.7548688924 10.210714272 12.6610096995 9.2710095615 9.9740362275 7.8742362126 11.83404805875 7.4232262062 9.881318457 7.9459382322 4.77908385705 7.2529208676 8.33240638656 8.3717862777 10.6407771795 6.92218155852 10.235332683 8.7011211054375 8.3159370171 6.8860125891 8.9881259796 9.9500141475 7.4748739698 6.90493641075 14.56366575825 9.755726832 9.79887911355 9.361035771 10.50772679325 8.0205376761 9.559687725 9.69976719306 7.33204983105 9.5639611905 12.07350841725 10.257278391 8.40207682485 9.65170163925 10.04264337285 9.589157204625 7.574147901 8.39888146305 10.508689665 8.74615486005 9.362670849 8.487329829 10.22216422005 12.4940259315 9.9426031866 9.44051631525 9.726331968 12.327415371 7.72634272455 7.671406428 10.827005048 13.142905947 7.91038934415 9.441026667 7.890122698425 11.521553535 8.83550620944 9.16143081 8.4066502464 10.11000732 10.308823797 9.811174005 9.872113659 8.6942594817 9.25278098535 8.98448207657143 7.23817119015 8.7961525677 6.9871327776 8.6818339233 10.4764679325 9.879103311 10.167683688 10.059101253 6.6254488254 9.9251445546 8.5538444805 9.91628503245 8.55789981678 8.56757690505 8.50029547815 7.0064499852 11.438867511 9.851058621 9.9724620465 7.78142908035 10.1552454054 10.501080074325 9.82652443254 10.3488673905 11.493552717 8.226867004875 9.745832053125 9.6302712975 9.99 14.407236237 9.0991127592 9.93313395 8.6906205882 8.5028042359125 8.481707253 8.9857084455 11.7931220955 10.710610929 9.254864925 9.427224267 10.095978015 9.851627853 6.08637363885 9.1076555646 9.230386743 8.02125 8.8947897849 8.01336965985 10.627366056 9.41991573204 8.861871168 8.0118189981 8.88880650435 7.2537528301875 9.0938215026 5.6185714287 5.46395126025 9.09 9.255222531 7.1735060331 8.5511062503 6.714773255025 7.5989190342 7.7777667021 7.08314550345 10.00809975456 7.1699055435 10.421080866 8.96961992355 9.814582467 10.527305913 10.1747481696 9.719537598 9.66292773435 8.6219188056 11.07904936545 7.915850364375 8.6709428694 7.271044524 10.119042828 10.0249519104 7.65305756046 9.318781314 5.6618631477 10.19497370805 7.2475431054 5.3106216426 9.27858797595 10.276650399 9.1344502395 10.150774383 10.558243176 11.9109819904 11.00131613925 9.352208208825 10.855514277 8.8866608382 11.351374458 8.99694908055 10.049029095 6.73709270076 9.0428734791 9.08074866735 8.9519343678 7.67915716635 11.10778978752 8.88780397455 8.99981426025 11.7397995615 8.50192278525 6.5839219596 6.6491117919 8.60720928 7.2219731391 8.2184387307 6.5351545353 10.84902945525 8.0584704702 11.0553963345 11.047201524 10.33750796625 11.5703551998 9.818552808 9.308813619 6.7678641786 8.818867028925 10.2461740875 10.2703260465 9.14298816195 11.0272241835 10.439616402 11.0873766015 10.1602635225 10.9161135385714 9.5775962157 9.29229814845 6.401737974375 10.5627436425 11.99322621 10.511042859 8.9793231874875 9.546847299 5.77818215145 8.95557661065 8.2710123771 8.9795596383 8.7052408938 10.966069509 11.228022717 9.088085808 9.821617773 10.340718876 10.107428994 10.355072129325 5.8218413787 8.2660760937 8.7643398816 7.4856923073 14.14032190425 9.6708208905 9.00576992115 7.58850911388 8.3155039011 9.323189514 9.26997295663636 9.27664389 8.654516835975 10.1439657405 6.194700621 7.5942822474 8.0314861473 8.7561192852 9.2640148632 8.076195666 9.488259018 9.922043502 9.3759528666 9.27963419472 8.78142360078 7.909202722275 7.3630888263 6.69458706525 8.342300058 7.561275354 8.4097773333 8.8749884286 10.115013069 8.90989606845 8.73073480635 8.51073283845 8.05256990595 8.0379204363 9.3258812964 9.2425448295 3.9265336872 9.1863298902 7.675913277 7.2883797453 6.6807969003 7.6983188607 9.18665339914286 7.8425980905 8.3066713542 8.5829490306 7.1105179545 6.3449659863 9.85070133 10.4202135642 9.234256185 8.736867190575 8.06693664645 8.44836225975 8.97729520185 9.1693368882 8.5955311107 9.197163466875 8.5881670029 9.7222338615 6.9162055029 9.368839674 8.3387526348 12.31559214 10.3242696075 8.35381422495 7.80015886335 8.55 7.3330079247 7.32644557785 7.49817853245 9.757790994 9.0494113956 5.692133823 6.65413885575 6.0902091864 9.60635718 9.318179799 9.42606657 9.2776377024 10.495248315 8.073130908525 8.4458402835 9.44977173075 8.6735036499 8.4296815905 9.58141386225 8.5576970232 9.662026563 5.7032179626 6.0306841881 9.3564939465 6.9379231563 9.796649242275 8.548573461 9.705241143 8.7133158927 11.730692466 8.9116139901 8.5033930266 8.7733218258 6.7554378882 10.510250787 9.5088596475 9.345776238 10.60792282107 7.486837445475 5.0172162363 9.90067709475 6.37774789815 8.2528014852 9.47471895 9.8678980485 8.69150194335 8.6260673871 9.984133701 9.990430425 9.987568974 9.980520984 7.3158001425 8.1718471857 15.183845199 10.0280691045 8.7911708922 8.07240120885 9.188926713 7.992 8.82681795 8.388 7.43792608395 8.2291097484 8.3259041607 8.2965511395 7.6444844481 9.204893613 9.11419331805 9.8212592847 9.2495546595 8.4291624999 9.659505065175
perc_crude_protein_diet upper 11.0459658943333 12.1933886433333 12.6460852686667 11.2980393702 11.2996307560714 10.4514489592333 8.24621993836667 10.3159075261 10.8584557282667 10.3184477418 11.779915169 11.7486258816667 8.9607630255 12.6876980041429 15.2163349341667 10.7776930096 10.98081748896 11.6678076218 9.97207052086667 12.1628327766 12.3599223332 6.3849888905 8.9876820979 6.95746370495 10.2725195221 11.6415001596667 9.66642224276667 9.20750914783333 11.874985632675 11.2447258 12.21394766075 11.0009375809667 7.81583374385 10.3696576159 11.082209123425 6.26940460135 12.652784441 5.3974852052 12.6619817005 9.16671233115 11.114264964 12.1273863406667 12.116732364 9.49349624135 10.70260441415 15.105285998 10.7003953129333 12.479761888 15.4745674105 11.3312339085 12.1904887225 9.62406648206667 14.46383651625 9.0728320298 12.077167003 9.7117022838 5.84110249195 8.8646810604 10.18405225024 10.2321832283 13.0053943305 8.46044412708 12.509851057 10.6347035733125 10.1639230209 8.4162376089 10.9854873084 12.1611284025 9.1359570742 8.43936672425 17.80003592675 11.923666128 11.97640780545 11.4412659423333 12.84277719175 9.8028793819 11.684062775 11.85527101374 8.96139423795 11.6892858995 14.75651028775 12.536673589 10.26920500815 11.79652422575 12.27434190015 11.720081027875 9.257291879 10.26529956595 12.843954035 10.68974482895 11.443264371 10.3734031243333 12.49375626895 15.2704761385 12.1520705614 11.53840882975 11.887739072 15.066841009 9.44330777445 9.376163412 13.2330061697778 16.063551713 9.66825364285 11.539032593 9.643483298075 14.081898765 10.79895203376 11.1973043233333 10.2747947456 12.3566756133333 12.5996735296667 11.991434895 12.0659166943333 10.6263171443 11.30895453765 10.9810336491429 8.84665367685 10.7508531383 8.5398289504 10.6111303507 12.8045719175 12.0744596023333 12.427168952 12.294457087 8.0977707866 12.1307322334 10.4546988095 12.11990392855 10.45965533162 10.47148288395 10.38925002885 8.5634388708 13.980838069 12.040182759 12.1885647235 9.51063554265 12.4119666066 12.834653424175 12.01019652866 12.6486156995 14.047675543 10.055059672625 11.911572509375 11.7703315858333 12.21 17.6088442896667 11.1211378168 12.14049705 10.6218696078 10.3923162883375 10.366531087 10.9825325445 14.4138158945 13.090746691 11.311501575 11.522162993 12.339528685 12.040878487 7.43890111415 11.1315790234 11.281583797 9.80375 10.8714097371 9.79411847315 12.9890029573333 11.51323033916 10.831175872 9.7922232199 10.86409683865 8.8656979035625 11.1146707254 6.8671428573 6.67816265141667 11.11 11.311938649 8.7676184849 10.4513520837 8.206945089475 9.28756770846667 9.50615930256667 8.65717783755 12.23212192224 8.7632178865 12.736876614 10.96286879545 11.995600793 12.866707227 12.4358033184 11.879434842 11.81024500865 10.5379007624 13.54106033555 9.674928223125 10.5978190626 8.886832196 12.367719012 12.2527190016 9.35373701834 11.389621606 6.9200549583 12.46052342095 8.85810823993333 6.4907597854 11.34049641505 12.5603504876667 11.1643280705 12.4065020236667 12.9045194373333 14.5578668771556 13.4460530590833 11.430476699675 13.267850783 10.8614743578 13.8739021153333 10.99627109845 12.2821466716667 8.23422441204 11.0524009189 11.09869281565 10.9412531162 9.38563653665 13.57618751808 10.86287152445 10.99977298475 14.3486439085 10.39123895975 8.0470157284 8.1266921901 10.5199224533333 8.8268560589 10.0447584486333 7.9874110987 13.25992488975 9.8492416858 13.5121510755 13.502135196 12.63473195875 14.1415452442 12.000453432 11.3774388676667 8.27183399606667 10.778615257575 12.5231016625 12.5526207235 11.17476330905 13.4777184465 12.759531158 13.5512380685 12.4180998608333 13.3419165471429 11.7059509303 11.35725329255 7.824346413125 12.9100200075 14.65838759 12.846830161 10.9747283402625 11.668368921 7.06222262955 10.94570474635 10.1090151275667 10.9750173357 10.6397388702 13.4029738443333 13.7231388763333 11.107660432 12.0041995003333 12.638656404 12.353524326 12.656199269175 7.1155839073 10.1029818923 10.7119709664 9.1491794867 17.28261566075 11.8198921995 11.00705212585 9.27484447252 10.1633936569 11.395009406 11.329966947 11.33812031 10.577742799525 12.3981803495 7.571300759 9.2819005246 9.8162608467 10.7019235708 11.3226848328 9.870905814 11.596761022 12.126942058 11.4594979480667 11.34177512688 10.73285106762 9.666803327225 8.9993307877 8.18227307975 10.1961445153333 9.241558766 10.2786167407 10.8472080794 12.362793751 10.88987297255 10.67089809665 10.40200680255 9.84202988505 9.8241249777 11.3982993622667 11.2964436805 4.7990967288 11.2277365324667 9.381671783 8.9080196887 8.1654184337 9.4090563853 11.2281319322857 9.58539766616667 10.1525983218 10.4902710374 8.6906330555 7.7549584277 12.03974607 12.7358165784667 11.286313115 10.678393232925 9.85958923455 10.32577609525 10.97224969115 11.2069673078 10.5056491353 11.240977570625 10.4966485591 11.8827302751667 8.4531400591 11.450804046 10.1918087758667 15.0523903933333 12.6185517425 10.21021738605 9.53352749965 10.45 8.9625652413 8.95454459515 9.16444042855 11.9261889926667 11.0603917057333 6.95705245033333 8.13283637925 7.4435890056 11.74110322 11.388886421 11.52074803 11.3393349696 12.8275257183333 9.86715999930833 10.3226936798333 11.54972100425 10.6009489054333 10.3029441661667 11.71061694275 10.4594074728 11.809143577 6.97059973206667 7.3708362299 11.4357148235 8.4796838577 11.973682407225 10.4482564523333 11.861961397 10.6496083133 14.337513014 10.8919726545667 10.3930359214 10.7229488982 8.2566463078 12.845862073 11.6219395691667 11.422615402 12.96523900353 9.150579100025 6.1321531777 12.10082756025 7.79502520885 10.0867573708 11.58021205 12.0607642815 10.62294681965 10.5429712509 12.202830079 12.210526075 12.207028746 12.198414536 8.9415335075 9.98781322696667 18.558033021 12.2565289055 10.7447644238 9.86626814415 11.230910427 9.768 10.78833305 10.252 9.09079854705 10.0578008036 10.1761050853 10.1402291705 9.3432587699 11.250425527 11.13956961095 12.0037613479667 11.3050112505 10.3023097221 11.806061746325
perc_crude_protein_diet distribution posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm posnorm

Here we run the model with the scenario_mc function. All variables that aren’t specified in the file called in the scenarios option remain at their original values, given in the usual input table, called with base_estimate.

GHG_simulation_scenarios<-scenario_mc(base_estimate = estimate_read_csv("data/livestock_ghg_input_table.csv"),
                                 scenarios = read.csv("data/farm_scenarios.csv",
                                                      fileEncoding = "UTF-8-BOM"),
                                 model_function = ghg_emissions,
                                 numberOfModelRuns = 10,
                                 functionSyntax = "plainNames")

Here we plot the distributions of the various outputs from the model.

# enteric_CH4=enteric_CH4,
enteric_CH4_dist_plot <- plot_distributions(mcSimulation_object = GHG_simulation_scenarios,
                   vars = "enteric_CH4",
                   method = "boxplot_density",
                   old_names = "enteric_CH4",
                   new_names = "Total Methane Emissions from Livestock by Enteric Fermentation") + 
  theme(axis.title.x=element_blank(),
        axis.title.y=element_blank())

# milk_yield=sum(total_milk),
milk_yield_dist_plot <- plot_distributions(mcSimulation_object = GHG_simulation_scenarios,
                   vars = "milk_yield",
                   method = "boxplot_density",
                   old_names = "milk_yield",
                   new_names = "Total Milk Yield") + 
  theme(axis.title.x=element_blank(),
        axis.title.y=element_blank())

# enteric_CH4_CO2eq=sum(enteric_CH4_CO2eq),
enteric_CH4_CO2eq_dist_plot <- plot_distributions(mcSimulation_object = GHG_simulation_scenarios,
                   vars = "enteric_CH4_CO2eq",
                   method = "boxplot_density",
                   old_names = "enteric_CH4_CO2eq",
                   new_names = "Carbon Dioxide Equivalent Enteric Methane Emissions from Livestock") + 
  theme(axis.title.x=element_blank())

# mm_CH4_CO2eq=sum(mm_CH4_CO2eq),
mm_CH4_CO2eq_dist_plot <- plot_distributions(mcSimulation_object = GHG_simulation_scenarios,
                   vars = "mm_CH4_CO2eq",
                   method = "boxplot_density",
                   old_names = "mm_CH4_CO2eq",
                   new_names = "Carbon Dioxide Equivalent Methane Emissions from Livestock Manure") + 
  theme(axis.title.x=element_blank(),
        axis.title.y=element_blank())

# mm_N2O_CO2eq=sum(mm_N2O_CO2eq),
mm_N2O_CO2eq_dist_plot <- plot_distributions(mcSimulation_object = GHG_simulation_scenarios,
                   vars = "mm_N2O_CO2eq",
                   method = "boxplot_density",
                   old_names = "mm_N2O_CO2eq",
                   new_names = "Carbon Dioxide Equivalent Nitrous Oxide Emissions from Livestock Manure") + 
  theme(axis.title.x=element_blank(),
        axis.title.y=element_blank())

# feed_CO2=feed_CO2,
feed_CO2_dist_plot <- plot_distributions(mcSimulation_object = GHG_simulation_scenarios,
                   vars = "feed_CO2",
                   method = "boxplot_density",
                   old_names = "feed_CO2",
                   new_names = "Carbon Dioxide Emissions from Livestock Feed Production and Transport") + 
  theme(axis.title.x=element_blank(),
        axis.title.y=element_blank())

# on_farm=sum(on_farm)
on_farm_dist_plot <- plot_distributions(mcSimulation_object = GHG_simulation_scenarios,
                   vars = "on_farm",
                   method = "boxplot_density",
                   old_names = "on_farm",
                   new_names = "Total Carbon Dioxide Equivalent Emissions from Livestock") + 
  theme(axis.title.y=element_blank())

library(patchwork)
# remove the axis marks from the two lefthand-most plots and the x-axis title from the left and right plots

# plot with patchwork
layout <- "
           AAABBB
           CCCDDD
           EEEFFF
           GGGGGG
          "

enteric_CH4_dist_plot + milk_yield_dist_plot + 
  enteric_CH4_CO2eq_dist_plot + mm_CH4_CO2eq_dist_plot + 
  mm_N2O_CO2eq_dist_plot + feed_CO2_dist_plot + 
  on_farm_dist_plot + 
plot_layout(guides = "collect", design = layout)

Normalizing milk yield and emissions per head

animals_per_run<-rowSums(GHG_simulation_scenarios$x[paste0("N_animal_type_",1:12)])

milk_yield_per_head<-GHG_simulation_scenarios$y$milk_yield/animals_per_run
total_emissions_per_head<-GHG_simulation_scenarios$y$on_farm/animals_per_run

Uncertainty for per-head data

As with the GHG Model, we apply the uncertainty functions on the milk_yield and on_farm variables. These come from the model results (4140) of the GHG_simulation_scenarios model results to the in_var (x) and an outcome variable out_var (y). In the model we defined on_farm as the sum of Carbon Dioxide Equivalent Enteric Methane Emissions from Livestock (enteric_CH4_CO2eq), Carbon Dioxide Equivalent Methane Emissions from Livestock Manure (mm_CH4_CO2eq), Carbon Dioxide Equivalent Nitrous Oxide Emissions from Livestock Manure (mm_N2O_CO2eq) and Carbon Dioxide Emissions from Livestock Feed Production and Transport (feed_CO2).

in_var  <- milk_yield_per_head # "Total Milk Yield"
out_var <- total_emissions_per_head # "Total emissions"

Milk_yield (x-axis) effect on on-farm GHG emissions (y-axis)

uncertainty::varscatter(in_var = in_var,
                        out_var = out_var,
                        xlab = expression(Total~milk~yield~(kg~head^{-1}~year^{-1})),
                        ylab = expression(Total~emissions~(kg~CO[2]-eq~head^{-1}~year^{-1})))

## [1] "Scatter plot of influencing variable (x) and outcome variable (y) with associated and estimated densities."
uncertainty::varkernel(in_var = in_var, 
                       out_var = out_var,
                        xlab = expression(Total~milk~yield~(kg~head^{-1}~year^{-1})),
                        ylab = expression(Total~emissions~(kg~CO[2]-eq~head^{-1}~year^{-1})))

## [1] "Density surface plot of an expected influential variable (x) and an outcome variable of interest (y)."

Probability of GHG emissions for a given range of milk yield

uncertainty::varkernelslicerange(in_var = in_var, 
                                 out_var = out_var, 
                                 min_in_var = round(min(in_var)),
                                 max_in_var = round(max(in_var)), 
                                 xlab_vars = "Total emissions given milk yield range")

## [1] "Relative probability (y) of the outcome variable for the given values of the influencing variable (x)."

Interventions

Here’s a wild idea: Can we simply change values in the GHG_simulation_scenarios$x table now and apply the model to the resulting table? We may not have to draw new values (and that actually wouldn’t even be good, because this would dilute the intervention effect).

To clearly characterize the impact of interventions, we should compare their impact with a counterfactual, i.e. with the same farms without the intervention. While this is usually impossible in practice, it is easy with a model. We just have to run the simulation again with identical inputs except for those that are affected by the intervention.

We already simulated emissions for a large population of farms, and we can now apply our interventions to this population by modifying certain parameters of the input tables. What we don’t want now, however, is to randomize all values again, since this would add additional noise to the results, which would mask the signal caused by the interventions.

We now first need a function that can apply our greenhouse gas emissions model to a pre-defined set of input values. We can find syntax that can achieve this in the mcSimulation function. Here it is (with slight modifications):

run_model<-function(x_data,model)
  {
  varnames<-colnames(x_data)[which(!colnames(x_data)=="Scenario")]
  model_function_ <- function(x) {
    
    e <- as.list(sapply(X = varnames, FUN = function(i) as.numeric(unlist(x[i]))))
    eval(expr = body(model), envir = e)
  }
  y <- do.call(what = rbind, args = lapply(X = apply(X = x_data, 
                                                     MARGIN = 1, FUN = model_function_), FUN = unlist))
  returnObject <- list(y = data.frame(y), x = data.frame(x_data))
  returnObject$call <- match.call()
  class(returnObject) <- cbind("mcSimulation", class(returnObject))
  return(returnObject)
}

The following code can precisely reproduce the results of the previous analysis:

res<-run_model(GHG_simulation_scenarios$x,ghg_emissions)

To simulate interventions, we can now feed this function with modified x_data data.frames.

Breed intervention

Introduction of higher-performance breeds may reduce greenhouse gas emissions. We simulate an intervention that replaces animals of low-performance breeds with animals of high-performance breeds.

To retain the farm-specific performance levels obtained from the farm survey, we simulate these impacts in a relative manner. For this, we first need to determine the performance and characteristics of each of the breeds. We’ll base this on the farm survey data (Kenyaherds dataset from above).

cows<-Kenyaherds[which(Kenyaherds$animal_type %in% 4:6),]

ggplot(cows, aes(factor(breed),milk_yield)) +
  geom_violin(draw_quantiles=c(0.5),fill="light green") +
  xlab("Breed") + ylab("Milk yield (kg per day)") + theme_bw()
## Warning: Groups with fewer than two data points have been dropped.

milk_summary<-cbind(aggregate(cows$milk_yield, by=list(cows$breed), FUN=median),table(cows$breed))
milk_summary<-milk_summary[,c(1,4,2)]
colnames(milk_summary)<-c("Breed","n","Median_milk_yield")
milk_summary
##   Breed   n Median_milk_yield
## 1     1 488          6.708090
## 2     2 134          4.999040
## 3     3  16          6.372821
## 4     4  20          5.538156
## 5     5  36          3.035786
## 6     7   1          7.217000
## 7     8   7          3.507483

This evaluation shows that the difference in median milk yields is relatively small, but that the milk yield distributions show a lot of variation that is apparently not explained by the breed. It is also apparent that the number of animals these estimates are based on varies considerably. Two breeds make up 88.6 % of the entire population, with others having very few animals, which means that the medians may be somewhat unreliable (especially for the single animal of breed 7).

Let’s define the intervention as follows:

All animals are replaced by high-performance animals of breed 1, which increases milk yields proportionally to the median milk yield differences. This means that milk yields are multiplied by the following factors:

milk_summary[,"Intervention_factor"]<-sapply(milk_summary$Median_milk_yield, function(x) min(x/milk_summary$Median_milk_yield[1],1))
                                         
milk_summary             
##   Breed   n Median_milk_yield Intervention_factor
## 1     1 488          6.708090           1.0000000
## 2     2 134          4.999040           0.7452256
## 3     3  16          6.372821           0.9500201
## 4     4  20          5.538156           0.8255936
## 5     5  36          3.035786           0.4525559
## 6     7   1          7.217000           1.0000000
## 7     8   7          3.507483           0.5228735

A change of the breed also causes changes in weight, mature weight and weight gain.

live_weights<-aggregate(Kenyaherds$live_weight_LW, by=list(Kenyaherds$animal_type,Kenyaherds$breed), FUN=median)
colnames(live_weights)<-c("animal_type","breed","median_live_weight")
live_weights[,"Intervention_factor"]<-NA
for(i in 1:nrow(live_weights))
  live_weights$Intervention_factor[i]<-live_weights$median_live_weight[i]/
     live_weights$median_live_weight[which(live_weights$animal_type==live_weights$animal_type[i] &                                                                                                            live_weights$breed==1)]

# mature weight should probably be changed, and the code below does this. However, the mature weight is currently the same for all the breeds, so this accomplished nothing.

mature_weights<-aggregate(Kenyaherds$mature_weight_MW, by=list(Kenyaherds$animal_type,Kenyaherds$breed), FUN=median)
colnames(mature_weights)<-c("animal_type","breed","median_mature_weight")
mature_weights[,"Intervention_factor"]<-NA
for(i in 1:nrow(mature_weights))
  mature_weights$Intervention_factor[i]<-mature_weights$median_mature_weight[i]/
     mature_weights$median_mature_weight[which(mature_weights$animal_type==mature_weights$animal_type[i] &                                                                                                            mature_weights$breed==1)]

# same for the weight gain - doesn't depend on the breed at the moment, so all the factors are 1

weight_gains<-aggregate(Kenyaherds$weight_gain_WG, by=list(Kenyaherds$animal_type,Kenyaherds$breed), FUN=median)
colnames(weight_gains)<-c("animal_type","breed","median_weight_gain")
weight_gains[,"Intervention_factor"]<-NA
for(i in 1:nrow(weight_gains))
  weight_gains$Intervention_factor[i]<-weight_gains$median_weight_gain[i]/
     weight_gains$median_weight_gain[which(weight_gains$animal_type==weight_gains$animal_type[i] &                                                                                                            weight_gains$breed==1)]
weight_gains$Intervention_factor[which(weight_gains$median_weight_gain==0)]<-1

Now we can modify the input table accordingly.

go through all the farms. Determine the ‘improvement’ coefficients (based on herd structure)

farms<-unique(Kenyaherds$QQNO)
breed_intervention<-data.frame(Farm=farms, milk_yield_4=1, milk_yield_5=1, milk_yield_6=1) 

Kenyaherds_interventions<-Kenyaherds[,c("QQNO",
                                        "system",
                                        "animal_type",
                                        "breed",
                                        "live_weight_LW",
                                        "mature_weight_MW",
                                        "weight_gain_WG",
                                        "milk_yield",
                                        "dig_energy",
                                        "CP.")]

Kenyaherds_interventions[,"milk_change"]<-0
cows<-which(Kenyaherds_interventions$animal_type %in% 4:6)
Kenyaherds_interventions$milk_change[cows]<- sapply(cows,function(x) 1/milk_summary$Intervention_factor[milk_summary$Breed==Kenyaherds_interventions$breed[x]]  )
  
milk_change<-aggregate(Kenyaherds_interventions$milk_change[cows], by=list(Kenyaherds_interventions$QQNO[cows]), FUN=mean)

breed_intervention$milk_yield_4[which(breed_intervention$Farm %in% milk_change[,1])]<-milk_change[,2]
breed_intervention$milk_yield_5<-breed_intervention$milk_yield_4
breed_intervention$milk_yield_6<-breed_intervention$milk_yield_4

for(i in 1:nrow(Kenyaherds_interventions))
  {Kenyaherds_interventions[i,"live_weight_change"]<-
    1/live_weights$Intervention_factor[which(live_weights$animal_type==Kenyaherds_interventions$animal_type[i]&
                                             live_weights$breed==Kenyaherds_interventions$breed[i])]
#   Kenyaherds_interventions[i,"mature_weight_change"]<-
#      1/mature_weights$Intervention_factor[which(mature_weights$animal_type==Kenyaherds_interventions$animal_type[i]&
#                                                 mature_weights$breed==Kenyaherds_interventions$breed[i])]
   Kenyaherds_interventions[i,"weight_gain_change"]<-
     1/weight_gains$Intervention_factor[which(weight_gains$animal_type==Kenyaherds_interventions$animal_type[i]&
                                              weight_gains$breed==Kenyaherds_interventions$breed[i])]
}
   
live_weight_agg<-aggregate(Kenyaherds_interventions$live_weight_change,
                           by=list(Kenyaherds_interventions$QQNO,Kenyaherds_interventions$animal_type), FUN=mean)
#mature_weight_agg<-aggregate(Kenyaherds_interventions$mature_weight_change,
#by=list(Kenyaherds_interventions$QQNO,Kenyaherds_interventions$animal_type), FUN=mean)
weight_gain_agg<-aggregate(Kenyaherds_interventions$weight_gain_change,
                           by=list(Kenyaherds_interventions$QQNO,Kenyaherds_interventions$animal_type), FUN=mean)

colnames(live_weight_agg)<-c("farm","animal_type","conv_factor")
#colnames(mature_weight_agg)<-c("farm","animal_type","conv_factor")
colnames(weight_gain_agg)<-c("farm","animal_type","conv_factor")

for(i in 1:nrow(live_weight_agg))
  breed_intervention[which(breed_intervention$Farm==live_weight_agg$farm[i]),
                     paste0("live_weight_LW_",live_weight_agg$animal_type[i])]<-
    live_weight_agg$conv_factor[i]

#for(i in 1:nrow(mature_weight_agg))
#  breed_intervention[which(breed_intervention$Farm==mature_weight_agg$farm[i]),
#                     paste0("mature_weight_LW_",mature_weight_agg$animal_type[i])]<-
#    mature_weight_agg$conv_factor[i]

for(i in 1:nrow(weight_gain_agg))
  breed_intervention[which(breed_intervention$Farm==weight_gain_agg$farm[i]),
                     paste0("weight_gain_WG_",weight_gain_agg$animal_type[i])]<-
    weight_gain_agg$conv_factor[i]

breed_intervention[is.na(breed_intervention)]<-1

go through the x file for each row take a random draw based on adoption rate (does farm adopt or not), then (possibly) apply factors

x_breed_intervention<-GHG_simulation_scenarios$x

adoption_rate<-0.6

for(i in 1:nrow(x_breed_intervention))
{if(rbinom(1,1,adoption_rate))
    x_breed_intervention[i,colnames(breed_intervention)[2:ncol(breed_intervention)]]<-
      x_breed_intervention[i,colnames(breed_intervention)[2:ncol(breed_intervention)]] * 
      breed_intervention[which(paste0("Farm_",breed_intervention$Farm)==x_breed_intervention$Scenario[i]),
                         2:ncol(breed_intervention)]
  # implement changes in x file
}
  breed_res<-run_model(x_breed_intervention,ghg_emissions)

  animals_per_run<-rowSums(breed_res$x[paste0("N_animal_type_",1:12)])

breed_milk_yield_per_head<-breed_res$y$milk_yield/animals_per_run
breed_total_emissions_per_head<-breed_res$y$on_farm/animals_per_run

Evaluate impact of breed intervention

Assumed adoption rate, based on all farms (even the ones that already have high-performance farms and therefore don’t get an ‘upgrade’): 60 %

uncertainty::varscatter(in_var = breed_milk_yield_per_head,
                        out_var = breed_total_emissions_per_head,
                        xlab = expression(Total~milk~yield~(kg~head^{-1}~year^{-1})),
                        ylab = expression(Total~emissions~(kg~CO[2]-eq~head^{-1}~year^{-1})))
## [1] "Scatter plot of influencing variable (x) and outcome variable (y) with associated and estimated densities."
uncertainty::varscatter(in_var = GHG_simulation_scenarios$y$milk_yield/animals_per_run,
                        out_var = GHG_simulation_scenarios$y$on_farm/animals_per_run,
                        xlab = expression(Total~milk~yield~(kg~head^{-1}~year^{-1})),
                        ylab = expression(Total~emissions~(kg~CO[2]-eq~head^{-1}~year^{-1})))
## [1] "Scatter plot of influencing variable (x) and outcome variable (y) with associated and estimated densities."
uncertainty::varkernel(in_var = breed_milk_yield_per_head, 
                       out_var = breed_total_emissions_per_head,
                        xlab = expression(Total~milk~yield~(kg~head^{-1}~year^{-1})),
                        ylab = expression(Total~emissions~(kg~CO[2]-eq~head^{-1}~year^{-1})))
## [1] "Density surface plot of an expected influential variable (x) and an outcome variable of interest (y)."
uncertainty::varkernel(in_var = GHG_simulation_scenarios$y$milk_yield/animals_per_run, 
                       out_var = GHG_simulation_scenarios$y$on_farm/animals_per_run,
                        xlab = expression(Total~milk~yield~(kg~head^{-1}~year^{-1})),
                        ylab = expression(Total~emissions~(kg~CO[2]-eq~head^{-1}~year^{-1})))
## [1] "Density surface plot of an expected influential variable (x) and an outcome variable of interest (y)."
uncertainty::varkernelslicerange(in_var = breed_res$y$milk_yield/animals_per_run, 
                                 out_var = breed_res$y$on_farm/animals_per_run, 
                                 min_in_var = round(min(breed_res$y$milk_yield/animals_per_run)),
                                 max_in_var = round(max(breed_res$y$milk_yield/animals_per_run)), 
                                 xlab_vars = "Total emissions given milk yield range")
## [1] "Relative probability (y) of the outcome variable for the given values of the influencing variable (x)."
uncertainty::varkernelslicerange(in_var = GHG_simulation_scenarios$y$milk_yield/animals_per_run, 
                                 out_var = GHG_simulation_scenarios$y$on_farm/animals_per_run, 
                                 min_in_var = round(min(GHG_simulation_scenarios$y$milk_yield/animals_per_run)),
                                 max_in_var = round(max(GHG_simulation_scenarios$y$milk_yield/animals_per_run)), 
                                 xlab_vars = "Total emissions given milk yield range")
## [1] "Relative probability (y) of the outcome variable for the given values of the influencing variable (x)."
Breed intervention (left) vs. baseline (right)Breed intervention (left) vs. baseline (right)Breed intervention (left) vs. baseline (right)Breed intervention (left) vs. baseline (right)Breed intervention (left) vs. baseline (right)Breed intervention (left) vs. baseline (right)

Breed intervention (left) vs. baseline (right) 

breed_change_in_CO2_eq_per_animal<-breed_res$y$on_farm/animals_per_run-
  GHG_simulation_scenarios$y$on_farm/animals_per_run

breed_change_in_milk_yield_per_animal<-breed_res$y$milk_yield/animals_per_run-
  GHG_simulation_scenarios$y$milk_yield/animals_per_run

Feed

Herd structure

This document was generated using R Markdown (Allaire et al. 2021).

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2021. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown.
Luedeling, Eike, Lutz Goehring, Katja Schiffers, Cory Whitney, and Eduardo Fernandez. 2021. decisionSupport: Quantitative Support of Decision Making Under Uncertainty. http://www.worldagroforestry.org/.